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Sample records for gene-based outcome prediction

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

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Christof Winter

Full Text Available Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.
Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

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Korkola, James E; Waldman, Frederic M; Blaveri, Ekaterina; DeVries, Sandy; Moore, Dan H II; Hwang, E Shelley; Chen, Yunn-Yi; Estep, Anne LH; Chew, Karen L; Jensen, Ronald H

2007-01-01

Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients
FERAL : Network-based classifier with application to breast cancer outcome prediction

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Allahyar, A.; De Ridder, J.

2015-01-01

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.
Module-based outcome prediction using breast cancer compendia.

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Martin H van Vliet

Full Text Available BACKGROUND: The availability of large collections of microarray datasets (compendia, or knowledge about grouping of genes into pathways (gene sets, is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors. METHODOLOGY: We extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset, and other institutions (inter-dataset. CONCLUSIONS: We show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data.
CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

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Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

2018-03-01

In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and
Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

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Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.
An algorithm to discover gene signatures with predictive potential

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Hallett Robin M

2010-09-01

Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.
Uncertainties in model-based outcome predictions for treatment planning

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Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

2001-01-01

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment
The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures

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Cockburn, Jessica G.; Hallett, Robin M.; Gillgrass, Amy E.; Dias, Kay N.; Whelan, T.; Levine, M. N.; Hassell, John A.; Bane, Anita

2016-01-01

Lymph node (LN) status is the most important prognostic variable used to guide ER positive (+) breast cancer treatment. While a positive nodal status is traditionally associated with a poor prognosis, a subset of these patients respond well to treatment and achieve long-term survival. Several gene signatures have been established as a means of predicting outcome of breast cancer patients, but the development and indication for use of these assays varies. Here we compare the capacity of two approved gene signatures and a third novel signature to predict outcome in distinct LN negative (-) and LN+ populations. We also examine biological differences between tumours associated with LN- and LN+ disease. Gene expression data from publically available data sets was used to compare the ability of Oncotype DX and Prosigna to predict Distant Metastasis Free Survival (DMFS) using an in silico platform. A novel gene signature (Ellen) was developed by including patients with both LN- and LN+ disease and using Prediction Analysis of Microarrays (PAM) software. Gene Set Enrichment Analysis (GSEA) was used to determine biological pathways associated with patient outcome in both LN- and LN+ tumors. The Oncotype DX gene signature, which only used LN- patients during development, significantly predicted outcome in LN- patients, but not LN+ patients. The Prosigna gene signature, which included both LN- and LN+ patients during development, predicted outcome in both LN- and LN+ patient groups. Ellen was also able to predict outcome in both LN- and LN+ patient groups. GSEA suggested that epigenetic modification may be related to poor outcome in LN- disease, whereas immune response may be related to good outcome in LN+ disease. We demonstrate the importance of incorporating lymph node status during the development of prognostic gene signatures. Ellen may be a useful tool to predict outcome of patients regardless of lymph node status, or for those with unknown lymph node status. Finally we
Network information improves cancer outcome prediction.

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Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

2014-07-01

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
Embryo quality predictive models based on cumulus cells gene expression

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Devjak R

2016-06-01

Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.
Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

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Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.
Gene Expression Profiling to Predict Clinical Outcome of Breast Cancer: reproducing, analyzing and extending the Nature publication by vhVeer et al

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Li R.; Visser, H.M.

2010-01-01

Chemotherapy and hormonal therapy as adjuvant systemic therapies to inhibit breast cancer recurrence are not necessary for each patient. In Veer's paper "Gene expression profiling predicts clinical outcome of breast cancer" (Nature 2002, PMID: 11823860), they introduced a method based on DNA
Predicting Recurrence and Progression of Noninvasive Papillary Bladder Cancer at Initial Presentation Based on Quantitative Gene Expression Profiles

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Birkhahn, M.; Mitra, A.P.; Williams, Johan

2010-01-01

% specificity. Since this is a small retrospective study using medium-throughput profiling, larger confirmatory studies are needed. Conclusions: Gene expression profiling across relevant cancer pathways appears to be a promising approach for Ta bladder tumor outcome prediction at initial diagnosis......Background: Currently, tumor grade is the best predictor of outcome at first presentation of noninvasive papillary (Ta) bladder cancer. However, reliable predictors of Ta tumor recurrence and progression for individual patients, which could optimize treatment and follow-up schedules based...... on specific tumor biology, are yet to be identified. Objective: To identify genes predictive for recurrence and progression in Ta bladder cancer at first presentation using a quantitative, pathway-specific approach. Design, setting, and participants: Retrospective study of patients with Ta G2/3 bladder tumors...
Protein-Based Urine Test Predicts Kidney Transplant Outcomes

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... News Releases News Release Thursday, August 22, 2013 Protein-based urine test predicts kidney transplant outcomes NIH- ... supporting development of noninvasive tests. Levels of a protein in the urine of kidney transplant recipients can ...
Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

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Kim Seon-Young

2009-05-01

Full Text Available Abstract Background Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature. Results A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+ patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures Conclusion Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.
Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

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Karlsson, Elin; Delle, Ulla; Danielsson, Anna; Olsson, Björn; Abel, Frida; Karlsson, Per; Helou, Khalil

2008-01-01

It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. The research on these tumours was approved by the Medical Faculty Research
Probability-based collaborative filtering model for predicting gene-disease associations.

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Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.
Prediction of metastasis from low-malignant breast cancer by gene expression profiling

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Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja

2007-01-01

examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients...... with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.......Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly...
The role of gene-gene interaction in the prediction of criminal behavior.

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Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

2014-04-01

A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

Prediction of highly expressed genes in microbes based on chromatin accessibility

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Ussery David W

2007-02-01

Full Text Available Abstract Background It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes. Results We find that absolute gene expression levels are correlated with the position preference in many microbial genomes. It is postulated that in these regions, the DNA may be more accessible to the transcriptional machinery. Moreover, ribosomal proteins and ribosomal RNA are encoded by DNA having significantly lower position preference values than other genes in fast-replicating microbes. Conclusion This insight into DNA structure-dependent gene expression in microbes may be exploited for predicting the expression of non-translated genes such as non-coding RNAs that may not be predicted by any of the conventional codon usage bias approaches.
GOBO: gene expression-based outcome for breast cancer online.

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Markus Ringnér

Full Text Available Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo, allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1 rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2 identification of co-expressed genes for creation of potential metagenes, 3 association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.
A network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer.

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Cao, Bangrong; Luo, Liping; Feng, Lin; Ma, Shiqi; Chen, Tingqing; Ren, Yuan; Zha, Xiao; Cheng, Shujun; Zhang, Kaitai; Chen, Changmin

2017-12-13

The clinical benefit of adjuvant chemotherapy for stage II colorectal cancer (CRC) is controversial. This study aimed to explore novel gene signature to predict outcome benefit of postoperative 5-Fu-based therapy in stage II CRC. Gene-expression profiles of stage II CRCs from two datasets with 5-Fu-based adjuvant chemotherapy (training dataset, n = 212; validation dataset, n = 85) were analyzed to identify the indicator. A systemic approach by integrating gene-expression and protein-protein interaction (PPI) network was implemented to develop the predictive signature. Kaplan-Meier curves and Cox proportional hazards model were used to determine the survival benefit of adjuvant chemotherapy. Experiments with shRNA knock-down were carried out to confirm the signature identified in this study. In the training dataset, we identified 44 PPI sub-modules, by which we separate patients into two clusters (1 and 2) having different chemotherapeutic benefit. A predictor of 11 PPI sub-modules (11-PPI-Mod) was established to discriminate the two sub-groups, with an overall accuracy of 90.1%. This signature was independently validated in an external validation dataset. Kaplan-Meier curves showed an improved outcome for patients who received adjuvant chemotherapy in Cluster 1 sub-group, but even worse survival for those in Cluster 2 sub-group. Similar results were found in both the training and the validation dataset. Multivariate Cox regression revealed an interaction effect between 11-PPI-Mod signature and adjuvant therapy treatment in the training dataset (RFS, p = 0.007; OS, p = 0.006) and the validation dataset (RFS, p = 0.002). From the signature, we found that PTGES gene was up-regulated in CRC cells which were more resistant to 5-Fu. Knock-down of PTGES indicated a growth inhibition and up-regulation of apoptotic markers induced by 5-Fu in CRC cells. Only a small proportion of stage II CRC patients could benefit from adjuvant therapy. The 11-PPI-Mod as
A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer

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C. Staiger (Christine); S. Cadot; R Kooter; M. Dittrich (Marcus); T. Müller (Tobias); G.W. Klau (Gunnar); L.F.A. Wessels (Lodewyk)

2012-01-01

htmlabstractRecently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically
Predicting outcome of status epilepticus.

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Leitinger, M; Kalss, G; Rohracher, A; Pilz, G; Novak, H; Höfler, J; Deak, I; Kuchukhidze, G; Dobesberger, J; Wakonig, A; Trinka, E

2015-08-01

Status epilepticus (SE) is a frequent neurological emergency complicated by high mortality and often poor functional outcome in survivors. The aim of this study was to review available clinical scores to predict outcome. Literature review. PubMed Search terms were "score", "outcome", and "status epilepticus" (April 9th 2015). Publications with abstracts available in English, no other language restrictions, or any restrictions concerning investigated patients were included. Two scores were identified: "Status Epilepticus Severity Score--STESS" and "Epidemiology based Mortality score in SE--EMSE". A comprehensive comparison of test parameters concerning performance, options, and limitations was performed. Epidemiology based Mortality score in SE allows detailed individualization of risk factors and is significantly superior to STESS in a retrospective explorative study. In particular, EMSE is very good at detection of good and bad outcome, whereas STESS detecting bad outcome is limited by a ceiling effect and uncertainty of correct cutoff value. Epidemiology based Mortality score in SE can be adapted to different regions in the world and to advances in medicine, as new data emerge. In addition, we designed a reporting standard for status epilepticus to enhance acquisition and communication of outcome relevant data. A data acquisition sheet used from patient admission in emergency room, from the EEG lab to intensive care unit, is provided for optimized data collection. Status Epilepticus Severity Score is easy to perform and predicts bad outcome, but has a low predictive value for good outcomes. Epidemiology based Mortality score in SE is superior to STESS in predicting good or bad outcome but needs marginally more time to perform. Epidemiology based Mortality score in SE may prove very useful for risk stratification in interventional studies and is recommended for individual outcome prediction. Prospective validation in different cohorts is needed for EMSE, whereas
Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

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Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

2016-11-11

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies
Predicting Recurrence and Progression of Noninvasive Papillary Bladder Cancer at Initial Presentation Based on Quantitative Gene Expression Profiles

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Birkhahn, M.; Mitra, A.P.; Williams, Johan

2010-01-01

Background: Currently, tumor grade is the best predictor of outcome at first presentation of noninvasive papillary (Ta) bladder cancer. However, reliable predictors of Ta tumor recurrence and progression for individual patients, which could optimize treatment and follow-up schedules based...... on specific tumor biology, are yet to be identified. Objective: To identify genes predictive for recurrence and progression in Ta bladder cancer at first presentation using a quantitative, pathway-specific approach. Design, setting, and participants: Retrospective study of patients with Ta G2/3 bladder tumors...... at initial presentation with three distinct clinical outcomes: absence of recurrence (n = 16), recurrence without progression (n = 16), and progression to carcinoma in situ or invasive disease (n = 16). Measurements: Expressions of 24 genes that feature in relevant pathways that are deregulated in bladder...
Module-Based Outcome Prediction Using Breast Cancer Compendia

NARCIS (Netherlands)

Van Vliet, M.H.; Klijn, C.N.; Wessels, L.F.; Reinders, M.J.T.

2007-01-01

Background. The availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while
The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data

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Ravit Arav-Boger

2008-01-01

Full Text Available A large number of CMV strains has been reported to circulate in the human population, and the biological significance of these strains is currently an active area of research. The analysis of complex genetic information may be limited using conventional phylogenetic techniques. We constructed artificial neural networks to determine their feasibility in predicting the outcome of congenital CMV disease (defined as presence of CMV symptoms at birth based on two data sets: 54 sequences of CMV gene UL144 obtained from 54 amniotic fluids of women who contracted acute CMV infection during their pregnancy, and 80 sequences of 4 genes (US28, UL144, UL146 and UL147 obtained from urine, saliva or blood of 20 congenitally infected infants that displayed different outcomes at birth. When data from all four genes was used in the 20-infants’ set, the artificial neural network model accurately identified outcome in 90% of cases. While US28 and UL147 had low yield in predicting outcome, UL144 and UL146 predicted outcome in 80% and 85% respectively when used separately. The model identified specific nucleotide positions that were highly relevant to prediction of outcome. The artificial neural network classified genotypes in agreement with classic phylogenetic analysis. We suggest that artificial neural networks can accurately and efficiently analyze sequences obtained from larger cohorts to determine specific outcomes.The ANN training and analysis code is commercially available from Optimal Neural Informatics (Pikesville, MD.
Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

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Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

2017-11-15

Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.

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Hoogendoorn, Mark; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter

2017-09-01

Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients, we are able to show that we can predict therapy outcome with an area under the curve of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants, it is hard to generalize the results, but they do show great potential in this type of information.
Predictive gene lists for breast cancer prognosis: A topographic visualisation study

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Lowe David

2008-04-01

Full Text Available Abstract Background The controversy surrounding the non-uniqueness of predictive gene lists (PGL of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE and the Locally Linear Embedding(LLE techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion The random correlation effect to an arbitrary outcome induced by small subset selection from very high
Hadamard Kernel SVM with applications for breast cancer outcome predictions.

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Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.
Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

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Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

2012-07-15

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.
Clustering gene expression data based on predicted differential effects of GV interaction.

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Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.
Gene expression signature of normal cell-of-origin predicts ovarian tumor outcomes.

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Melissa A Merritt

Full Text Available The potential role of the cell-of-origin in determining the tumor phenotype has been raised, but not adequately examined. We hypothesized that distinct cells-of-origin may play a role in determining ovarian tumor phenotype and outcome. Here we describe a new cell culture medium for in vitro culture of paired normal human ovarian (OV and fallopian tube (FT epithelial cells from donors without cancer. While these cells have been cultured individually for short periods of time, to our knowledge this is the first long-term culture of both cell types from the same donors. Through analysis of the gene expression profiles of the cultured OV/FT cells we identified a normal cell-of-origin gene signature that classified primary ovarian cancers into OV-like and FT-like subgroups; this classification correlated with significant differences in clinical outcomes. The identification of a prognostically significant gene expression signature derived solely from normal untransformed cells is consistent with the hypothesis that the normal cell-of-origin may be a source of ovarian tumor heterogeneity and the associated differences in tumor outcome.
Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

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Winder, Anthony J.; Siemonsen, Susanne; Flottmann, Fabian; Fiehler, Jens; Forkert, Nils D.

2017-03-01

Voxel-based tissue outcome prediction in acute ischemic stroke patients is highly relevant for both clinical routine and research. Previous research has shown that features extracted from baseline multi-parametric MRI datasets have a high predictive value and can be used for the training of classifiers, which can generate tissue outcome predictions for both intravenous and conservative treatments. However, with the recent advent and popularization of intra-arterial thrombectomy treatment, novel research specifically addressing the utility of predictive classi- fiers for thrombectomy intervention is necessary for a holistic understanding of current stroke treatment options. The aim of this work was to develop three clinically viable tissue outcome prediction models using approximate nearest-neighbor, generalized linear model, and random decision forest approaches and to evaluate the accuracy of predicting tissue outcome after intra-arterial treatment. Therefore, the three machine learning models were trained, evaluated, and compared using datasets of 42 acute ischemic stroke patients treated with intra-arterial thrombectomy. Classifier training utilized eight voxel-based features extracted from baseline MRI datasets and five global features. Evaluation of classifier-based predictions was performed via comparison to the known tissue outcome, which was determined in follow-up imaging, using the Dice coefficient and leave-on-patient-out cross validation. The random decision forest prediction model led to the best tissue outcome predictions with a mean Dice coefficient of 0.37. The approximate nearest-neighbor and generalized linear model performed equally suboptimally with average Dice coefficients of 0.28 and 0.27 respectively, suggesting that both non-linearity and machine learning are desirable properties of a classifier well-suited to the intra-arterial tissue outcome prediction problem.
Clinicopathologic and gene expression parameters predict liver cancer prognosis

International Nuclear Information System (INIS)

Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

2011-01-01

The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome
Outcome prediction in home- and community-based brain injury rehabilitation using the Mayo-Portland Adaptability Inventory.

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Malec, James F; Parrot, Devan; Altman, Irwin M; Swick, Shannon

2015-01-01

The objective of the study was to develop statistical formulas to predict levels of community participation on discharge from post-hospital brain injury rehabilitation using retrospective data analysis. Data were collected from seven geographically distinct programmes in a home- and community-based brain injury rehabilitation provider network. Participants were 642 individuals with post-traumatic brain injury. Interventions consisted of home- and community-based brain injury rehabilitation. The main outcome measure was the Mayo-Portland Adaptability Inventory (MPAI-4) Participation Index. Linear discriminant models using admission MPAI-4 Participation Index score and log chronicity correctly predicted excellent (no to minimal participation limitations), very good (very mild participation limitations), good (mild participation limitations), and limited (significant participation limitations) outcome levels at discharge. Predicting broad outcome categories for post-hospital rehabilitation programmes based on admission assessment data appears feasible and valid. Equations to provide patients and families with probability statements on admission about expected levels of outcome are provided. It is unknown to what degree these prediction equations can be reliably applied and valid in other settings.
Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

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Borlawsky Tara B

2010-10-01

Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. Conclusions We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

Prediction of Metastasis and Recurrence in Colorectal Cancer Based on Gene Expression Analysis: Ready for the Clinic?

Energy Technology Data Exchange (ETDEWEB)

Shibayama, Masaki [Sysmex Corporation, Central Research Laboratories, Kobe 651-2271 (Japan); Maak, Matthias; Nitsche, Ulrich [Chirurgische Klinik, Klinikum Rechts der Isar der TUM, München 81657 (Germany); Gotoh, Kengo [Sysmex Corporation, Central Research Laboratories, Kobe 651-2271 (Japan); Rosenberg, Robert; Janssen, Klaus-Peter, E-mail: klaus-peter.janssen@lrz.tum.de [Chirurgische Klinik, Klinikum Rechts der Isar der TUM, München 81657 (Germany)

2011-07-07

Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer.
Prediction of Metastasis and Recurrence in Colorectal Cancer Based on Gene Expression Analysis: Ready for the Clinic?

International Nuclear Information System (INIS)

Shibayama, Masaki; Maak, Matthias; Nitsche, Ulrich; Gotoh, Kengo; Rosenberg, Robert; Janssen, Klaus-Peter

2011-01-01

Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer
Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

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Lei Yang

2014-01-01

Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.
A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

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Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.
Action-outcome learning and prediction shape the window of simultaneity of audiovisual outcomes.

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Desantis, Andrea; Haggard, Patrick

2016-08-01

To form a coherent representation of the objects around us, the brain must group the different sensory features composing these objects. Here, we investigated whether actions contribute in this grouping process. In particular, we assessed whether action-outcome learning and prediction contribute to audiovisual temporal binding. Participants were presented with two audiovisual pairs: one pair was triggered by a left action, and the other by a right action. In a later test phase, the audio and visual components of these pairs were presented at different onset times. Participants judged whether they were simultaneous or not. To assess the role of action-outcome prediction on audiovisual simultaneity, each action triggered either the same audiovisual pair as in the learning phase ('predicted' pair), or the pair that had previously been associated with the other action ('unpredicted' pair). We found the time window within which auditory and visual events appeared simultaneous increased for predicted compared to unpredicted pairs. However, no change in audiovisual simultaneity was observed when audiovisual pairs followed visual cues, rather than voluntary actions. This suggests that only action-outcome learning promotes temporal grouping of audio and visual effects. In a second experiment we observed that changes in audiovisual simultaneity do not only depend on our ability to predict what outcomes our actions generate, but also on learning the delay between the action and the multisensory outcome. When participants learned that the delay between action and audiovisual pair was variable, the window of audiovisual simultaneity for predicted pairs increased, relative to a fixed action-outcome pair delay. This suggests that participants learn action-based predictions of audiovisual outcome, and adapt their temporal perception of outcome events based on such predictions. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
A polynomial based model for cell fate prediction in human diseases.

Science.gov (United States)

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.
Whole genome transcript profiling of drug induced steatosis in rats reveals a gene signature predictive of outcome.

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Nishika Sahini

Full Text Available Drug induced steatosis (DIS is characterised by excess triglyceride accumulation in the form of lipid droplets (LD in liver cells. To explore mechanisms underlying DIS we interrogated the publically available microarray data from the Japanese Toxicogenomics Project (TGP to study comprehensively whole genome gene expression changes in the liver of treated rats. For this purpose a total of 17 and 12 drugs which are diverse in molecular structure and mode of action were considered based on their ability to cause either steatosis or phospholipidosis, respectively, while 7 drugs served as negative controls. In our efforts we focused on 200 genes which are considered to be mechanistically relevant in the process of lipid droplet biogenesis in hepatocytes as recently published (Sahini and Borlak, 2014. Based on mechanistic considerations we identified 19 genes which displayed dose dependent responses while 10 genes showed time dependency. Importantly, the present study defined 9 genes (ANGPTL4, FABP7, FADS1, FGF21, GOT1, LDLR, GK, STAT3, and PKLR as signature genes to predict DIS. Moreover, cross tabulation revealed 9 genes to be regulated ≥10 times amongst the various conditions and included genes linked to glucose metabolism, lipid transport and lipogenesis as well as signalling events. Additionally, a comparison between drugs causing phospholipidosis and/or steatosis revealed 26 genes to be regulated in common including 4 signature genes to predict DIS (PKLR, GK, FABP7 and FADS1. Furthermore, a comparison between in vivo single dose (3, 6, 9 and 24 h and findings from rat hepatocyte studies (2 h, 8 h, 24 h identified 10 genes which are regulated in common and contained 2 DIS signature genes (FABP7, FGF21. Altogether, our studies provide comprehensive information on mechanistically linked gene expression changes of a range of drugs causing steatosis and phospholipidosis and encourage the screening of DIS signature genes at the preclinical stage.
Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

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Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

2004-03-05

Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.
Polymorphisms in XPD gene could predict clinical outcome of platinum-based chemotherapy for non-small cell lung cancer patients: a meta-analysis of 24 studies.

Directory of Open Access Journals (Sweden)

Qin Qin

Full Text Available OBJECTIVE: Xeroderma pigmentosum group D (XPD is an essential gene involved in the nucleotide excision repair (NER pathway. Two commonly studied single nucleotide polymorphisms (SNPs of XPD (Lys751Gln, A>C, rs13181; Asp312Asn, G>A, rs1799793 are implicated in the modulation of DNA repair capacity, thus related to the responses to platinum-based chemotherapy. Here we performed a meta-analysis to better evaluate the association between the two XPD SNPs and clinical outcome of platinum-based chemotherapy in non-small cell lung cancer (NSCLC patients. METHODS: A comprehensive search of PubMed database was conducted to identify relevant articles. Primary outcomes included objective response (i.e., complete response + partial response vs. stable disease + progressive disease, progression-free survival (PFS and overall survival (OS. The pooled and 95% confidence intervals (CIs of ORs (odds ratios and HRs (hazard ratios were estimated using the fixed or random effect model. RESULTS: Twenty-four studies were eligible according to the inclusion criteria. None of the XPD Lys751Gln/Asp312Asn polymorphisms was associated with objective response, PFS or OS in NSCLC patients treated with platinum drugs. However, in stratified analysis by ethnicity, the XPD Lys751Gln (A>C polymorphism was not significantly associated with increased response in Caucasians (OR=1.35, 95%CI=1.0-1.83, P=0.122 for heterogeneity but was associated with decreased PFS in Asians (HR=1.39, 95%CI=1.07-1.81, P=0.879 for heterogeneity. Furthermore, a statistically significant difference existed in the estimates of effect between the two ethnicities (P=0.014 for TR; PC may have inverse predictive and prognostic role in platinum-based treatment of NSCLC according to different ethnicities. Further studies are needed to validate our findings.
A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data

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Feng, Shou; Fu, Ping; Zheng, Wenbin

2018-03-01

Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is usually needed. This paper proposed a novel preliminary results processing method called the nodes interaction method. The nodes interaction method revises the preliminary results and guarantees that the predictions are consistent with the hierarchy constraint. This method exploits the label dependency and considers the hierarchical interaction between nodes when making decisions based on the Bayesian network in its first phase. In the second phase, this method further adjusts the results according to the hierarchy constraint. Implementing the nodes interaction method in the HMC framework also enhances the HMC performance for solving the gene function prediction problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph that is more difficult to tackle. The experimental results validate the promising performance of the proposed method compared to state-of-the-art methods on eight benchmark yeast data sets annotated by the GO.
Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation.

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Li, Min; Zhang, Jiayi; Liu, Qing; Wang, Jianxin; Wu, Fang-Xiang

2014-01-01

Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.
A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients. PMID:28152063
A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

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Mumtaz, Wajid; Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad; Malik, Aamir Saeed

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

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Ruzzo Walter L

2006-03-01

Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.
lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

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Silu Zhang

2018-01-01

Full Text Available Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs, which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.
Identification of epigenetically regulated genes that predict patient outcome in neuroblastoma

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Carén, Helena; Djos, Anna; Nethander, Maria; Sjöberg, Rose-Marie; Kogner, Per; Enström, Camilla; Nilsson, Staffan; Martinsson, Tommy

2011-01-01

Epigenetic mechanisms such as DNA methylation and histone modifications are important regulators of gene expression and are frequently involved in silencing tumor suppressor genes. In order to identify genes that are epigenetically regulated in neuroblastoma tumors, we treated four neuroblastoma cell lines with the demethylating agent 5-Aza-2'-deoxycytidine (5-Aza-dC) either separately or in conjunction with the histone deacetylase inhibitor trichostatin A (TSA). Expression was analyzed using whole-genome expression arrays to identify genes activated by the treatment. These data were then combined with data from genome-wide DNA methylation arrays to identify candidate genes silenced in neuroblastoma due to DNA methylation. We present eight genes (KRT19, PRKCDBP, SCNN1A, POU2F2, TGFBI, COL1A2, DHRS3 and DUSP23) that are methylated in neuroblastoma, most of them not previously reported as such, some of which also distinguish between biological subsets of neuroblastoma tumors. Differential methylation was observed for the genes SCNN1A (p < 0.001), PRKCDBP (p < 0.001) and KRT19 (p < 0.01). Among these, the mRNA expression of KRT19 and PRKCDBP was significantly lower in patients that have died from the disease compared with patients with no evidence of disease (fold change -8.3, p = 0.01 for KRT19 and fold change -2.4, p = 0.04 for PRKCDBP). In our study, a low methylation frequency of SCNN1A, PRKCDBP and KRT19 is significantly associated with favorable outcome in neuroblastoma. It is likely that analysis of specific DNA methylation will be one of several methods in future patient therapy stratification protocols for treatment of childhood neuroblastomas
Reranking candidate gene models with cross-species comparison for improved gene prediction

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Pereira Fernando CN

2008-10-01

Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.
Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

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Ge Cheng; Zhenyu Zhang; Moses Ntanda Kyebambe; Nasser Kimbugwe

2016-01-01

Predicting the outcome of National Basketball Association (NBA) matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that...
Validity of a simple Internet-based outcome-prediction tool in patients with total hip replacement: a pilot study.

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Stöckli, Cornel; Theiler, Robert; Sidelnikov, Eduard; Balsiger, Maria; Ferrari, Stephen M; Buchzig, Beatus; Uehlinger, Kurt; Riniker, Christoph; Bischoff-Ferrari, Heike A

2014-04-01

We developed a user-friendly Internet-based tool for patients undergoing total hip replacement (THR) due to osteoarthritis to predict their pain and function after surgery. In the first step, the key questions were identified by statistical modelling in a data set of 375 patients undergoing THR. Based on multiple regression, we identified the two most predictive WOMAC questions for pain and the three most predictive WOMAC questions for functional outcome, while controlling for comorbidity, body mass index, age, gender and specific comorbidities relevant to the outcome. In the second step, a pilot study was performed to validate the resulting tool against the full WOMAC questionnaire among 108 patients undergoing THR. The mean difference between observed (WOMAC) and model-predicted value was -1.1 points (95% confidence interval, CI -3.8, 1.5) for pain and -2.5 points (95% CI -5.3, 0.3) for function. The model-predicted value was within 20% of the observed value in 48% of cases for pain and in 57% of cases for function. The tool demonstrated moderate validity, but performed weakly for patients with extreme levels of pain and extreme functional limitations at 3 months post surgery. This may have been partly due to early complications after surgery. However, the outcome-prediction tool may be useful in helping patients to become better informed about the realistic outcome of their THR.
Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

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Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

2008-10-20

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

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Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.
Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

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Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

2014-01-01

Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.
Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

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Ge Cheng

2016-12-01

Full Text Available Predicting the outcome of National Basketball Association (NBA matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that the model is able to predict the winning team with 74.4% accuracy, outperforming other classical machine learning algorithms that could only afford a maximum prediction accuracy of 70.6% in the experiments that we performed.
A deep auto-encoder model for gene expression prediction.

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Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.
A pharmacogenetics study to predict outcome in patients receiving anti-VEGF therapy in age related macular degeneration

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Kitchens, John W; Kassem, Nawal; Wood, William; Stone, Thomas W; Isernhagen, Rick; Wood, Edward; Hancock, Brad A; Radovich, Milan; Waymire, Josh; Li, Lang; Schneider, Bryan P

2013-01-01

Purpose To ascertain whether single nucleotide polymorphisms (SNPs) in the Vascular Endothelial Growth factor (VEGFA), Complement Factor H (CFH), and LOC387715 genes could predict outcome to anti-VEGF therapy for patients with age related macular degeneration (AMD). Methods Patients with “wet” AMD were identified by chart review. Baseline optical coherence tomography (OCT) and visual acuity (VA) data, and at least 6 months of clinical follow up after 3 initial monthly injections of bevacizumab or ranibizumab were required for inclusion. Based on OCT and VA, patients were categorized into two possible clinical outcomes: (a) responders and (b) non-responders. DNA was extracted from saliva and genotyped for candidate SNPs in the VEGFA, LOC387715, and CFH genes. Clinical outcomes were statistically compared to patient genotypes. Results 101 patients were recruited, and one eye from each patient was included in the analysis. 97% of samples were successfully genotyped for all SNPs. We found a statistically significant association between the LOC387715 A69S TT genotype and outcome based on OCT. Conclusion Genetic variation may be associated with outcome in patients receiving anti-VEGF therapy. PMID:24143065
Prediction of lymphatic metastasis based on gene expression profile analysis after brachytherapy for early-stage oral tongue carcinoma

International Nuclear Information System (INIS)

Watanabe, Hiroshi; Mogushi, Kaoru; Miura, Masahiko; Yoshimura, Ryo-ichi; Kurabayashi, Tohru; Shibuya, Hitoshi; Tanaka, Hiroshi; Noda, Shuhei; Iwakawa, Mayumi; Imai, Takashi

2008-01-01

Background and purpose: The management of lymphatic metastasis of early-stage oral tongue carcinoma patients is crucial for its prognosis. The purpose of this study was to evaluate the predictive ability of lymphatic metastasis after brachytherapy (BRT) for early-stage tongue carcinoma based on gene expression profiling. Patients and methods: Pre-therapeutic biopsies from 39 patients with T1 or T2 tongue cancer were analyzed for gene expression signatures using Codelink Uniset Human 20K Bioarray. All patients were treated with low dose-rate BRT for their primary lesions and underwent strict follow-up under a wait-and-see policy for cervical lymphatic metastasis. Candidate genes were selected for predicting lymph-node status in the reference group by the permutation test. Predictive accuracy was further evaluated by the prediction strength (PS) scoring system using an independent validation group. Results: We selected a set of 19 genes whose expression differed significantly between classes with or without lymphatic metastasis in the reference group. The lymph-node status in the validation group was predicted by the PS scoring system with an accuracy of 76%. Conclusions: Gene expression profiling using 19 genes in primary tumor tissues may allow prediction of lymphatic metastasis after BRT for early-stage oral tongue carcinoma
Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis

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Li He

2014-01-01

Full Text Available For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.
Entropy-based gene ranking without selection bias for the predictive classification of microarray data

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Serafini Maria

2003-11-01

Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.
The integration of weighted human gene association networks based on link prediction.

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Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

2017-01-31

Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.
Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

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Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

2009-02-01

Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.
MO-AB-BRA-10: Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

Energy Technology Data Exchange (ETDEWEB)

Lian, C [Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne (France); University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France); Li, H; Chen, H; Robinson, C. [Washington University School of Medicine, Saint Louis, MO (United States); Denoeux, T [Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne (France); Vera, P [Centre Henri-Becquerel, 76038 Rouen (France); University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France); Ruan, S [University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France)

2015-06-15

Purpose: In cancer therapy, utilizing FDG-18 PET image-based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster-Shafer theory (DST) based approach, evidential low-dimensional transformation with feature selection (ELT-FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics. Methods: First, a specific loss function with sparse penalty was developed to learn an adaptive low-rank distance metric for representing the dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low-dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1-norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K-nearest-neighbor (EK- NN) classifier to predict treatment outcome. Results: Twenty-five patients with stage II–III non-small-cell lung cancer and thirty-six patients with esophageal squamous cell carcinomas treated with chemo-radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave-one-out cross-validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT-FS leads to higher prediction accuracy for the training and testing sets both for lung-cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal-cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT-FS also provides superior class separation in both test data sets. Conclusion: A novel DST- based approach has been proposed to predict cancer treatment outcome using PET
Outcome Prediction after Radiotherapy with Medical Big Data.

Science.gov (United States)

Magome, Taiki

2016-01-01

Data science is becoming more important in many fields. In medical physics field, we are facing huge data every day. Treatment outcomes after radiation therapy are determined by complex interactions between clinical, biological, and dosimetrical factors. A key concept of recent radiation oncology research is to predict the outcome based on medical big data for personalized medicine. Here, some reports, which are analyzing medical databases with machine learning techniques, were reviewed and feasibility of outcome prediction after radiation therapy was discussed. In addition, some strategies for saving manual labors to analyze huge data in medical physics were discussed.
Vertebrate gene predictions and the problem of large genes

DEFF Research Database (Denmark)

Wang, Jun; Li, ShengTing; Zhang, Yong

2003-01-01

To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistent...
Gene prediction validation and functional analysis of redundant pathways

DEFF Research Database (Denmark)

Sønderkær, Mads

2011-01-01

have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...
Prediction of highly expressed genes in microbes based on chromatin accessibility

DEFF Research Database (Denmark)

Willenbrock, Hanni; Ussery, David

2007-01-01

BACKGROUND: It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed...
Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence.

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Siristatidis, Charalampos; Vogiatzi, Paraskevi; Pouliakis, Abraham; Trivella, Marialenna; Papantoniou, Nikolaos; Bettocchi, Stefano

2016-01-01

To propose a functional in vitro fertilization (IVF) prediction model to assist clinicians in tailoring personalized treatment of subfertile couples and improve assisted reproduction outcome. Construction and evaluation of an enhanced web-based system with a novel Artificial Neural Network (ANN) architecture and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and "trained" by a network expert in an IVF setting. The system is capable to act as a routine information technology platform for the IVF unit and is capable of recalling and evaluating a vast amount of information in a rapid and automated manner to provide an objective indication on the outcome of an artificial reproductive cycle. ANNs are an exceptional candidate in providing the fertility specialist with numerical estimates to promote personalization of healthcare and adaptation of the course of treatment according to the indications. Copyright © 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.
Exploring the Optimal Strategy to Predict Essential Genes in Microbes

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Yao Lu

2011-12-01

Full Text Available Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.
Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

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Juan, Elsa; De Lucia, Marzia; Tzovara, Athina; Beaud, Valérie; Oddo, Mauro; Clarke, Stephanie; Rossetti, Andrea O

2016-09-01

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Semi-supervised prediction of gene regulatory networks using ...

Indian Academy of Sciences (India)

2015-09-28

Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.
Perceived Masculinity Predicts U.S. Supreme Court Outcomes

Science.gov (United States)

2016-01-01

Previous studies suggest a significant role of language in the court room, yet none has identified a definitive correlation between vocal characteristics and court outcomes. This paper demonstrates that voice-based snap judgments based solely on the introductory sentence of lawyers arguing in front of the Supreme Court of the United States predict outcomes in the Court. In this study, participants rated the opening statement of male advocates arguing before the Supreme Court between 1998 and 2012 in terms of masculinity, attractiveness, confidence, intelligence, trustworthiness, and aggressiveness. We found significant correlation between vocal characteristics and court outcomes and the correlation is specific to perceived masculinity even when judgment of masculinity is based only on less than three seconds of exposure to a lawyer’s speech sample. Specifically, male advocates are more likely to win when they are perceived as less masculine. No other personality dimension predicts court outcomes. While this study does not aim to establish any causal connections, our findings suggest that vocal characteristics may be relevant in even as solemn a setting as the Supreme Court of the United States. PMID:27737008

Perceived Masculinity Predicts U.S. Supreme Court Outcomes.

Directory of Open Access Journals (Sweden)

Daniel Chen

Full Text Available Previous studies suggest a significant role of language in the court room, yet none has identified a definitive correlation between vocal characteristics and court outcomes. This paper demonstrates that voice-based snap judgments based solely on the introductory sentence of lawyers arguing in front of the Supreme Court of the United States predict outcomes in the Court. In this study, participants rated the opening statement of male advocates arguing before the Supreme Court between 1998 and 2012 in terms of masculinity, attractiveness, confidence, intelligence, trustworthiness, and aggressiveness. We found significant correlation between vocal characteristics and court outcomes and the correlation is specific to perceived masculinity even when judgment of masculinity is based only on less than three seconds of exposure to a lawyer's speech sample. Specifically, male advocates are more likely to win when they are perceived as less masculine. No other personality dimension predicts court outcomes. While this study does not aim to establish any causal connections, our findings suggest that vocal characteristics may be relevant in even as solemn a setting as the Supreme Court of the United States.
Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

Science.gov (United States)

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.
Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

KAUST Repository

Hou, Siqing

2018-05-21

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.
A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

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Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

2006-01-01

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943
Microarray-based cancer prediction using soft computing approach.

Science.gov (United States)

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.
Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations

NARCIS (Netherlands)

Hoogendoorn, M.; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter

2016-01-01

Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more
Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential.

Science.gov (United States)

Tilton, Susan C; Siddens, Lisbeth K; Krueger, Sharon K; Larkin, Andrew J; Löhr, Christiane V; Williams, David E; Baird, William M; Waters, Katrina M

2015-07-01

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC > BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Comparison of Physician-Predicted to Measured Low Vision Outcomes

Science.gov (United States)

Chan, Tiffany L.; Goldstein, Judith E.; Massof, Robert W.

2013-01-01

Purpose To compare low vision rehabilitation (LVR) physicians’ predictions of the probability of success of LVR to patients’ self-reported outcomes after provision of usual outpatient LVR services; and to determine if patients’ traits influence physician ratings. Methods The Activity Inventory (AI), a self-report visual function questionnaire, was administered pre and post-LVR to 316 low vision patients served by 28 LVR centers that participated in a collaborative observational study. The physical component of the Short Form-36, Geriatric Depression Scale, and Telephone Interview for Cognitive Status were also administered pre-LVR to measure physical capability, depression and cognitive status. Following patient evaluation, 38 LVR physicians estimated the probability of outcome success (POS), using their own criteria. The POS ratings and change in functional ability were used to assess the effects of patients’ baseline traits on predicted outcomes. Results A regression analysis with a hierarchical random effects model showed no relationship between LVR physician POS estimates and AI-based outcomes. In another analysis, Kappa statistics were calculated to determine the probability of agreement between POS and AI-based outcomes for different outcome criteria. Across all comparisons, none of the kappa values were significantly different from 0, which indicates the rate of agreement is equivalent to chance. In an exploratory analysis, hierarchical mixed effects regression models show that POS ratings are associated with information about the patient’s cognitive functioning and the combination of visual acuity and functional ability, as opposed to visual acuity or functional ability alone. Conclusions Physicians’ predictions of LVR outcomes appear to be influenced by knowledge of patients’ cognitive functioning and the combination of visual acuity and functional ability - information physicians acquire from the patient’s history and examination. However
Combining gene prediction methods to improve metagenomic gene annotation

Directory of Open Access Journals (Sweden)

Rosen Gail L

2011-01-01

Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.
Combining Gene Signatures Improves Prediction of Breast Cancer Survival

Science.gov (United States)

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast
Combining gene signatures improves prediction of breast cancer survival.

Directory of Open Access Journals (Sweden)

Xi Zhao

Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves
Genetic risk for schizophrenia, obstetric complications, and adolescent school outcome: evidence for gene-environment interaction.

Science.gov (United States)

Forsyth, Jennifer K; Ellman, Lauren M; Tanskanen, Antti; Mustonen, Ulla; Huttunen, Matti O; Suvisaari, Jaana; Cannon, Tyrone D

2013-09-01

Low birth weight (LBW) and hypoxia are among the environmental factors most reliably associated with schizophrenia; however, the nature of this relationship is unclear and both gene-environment interaction and gene-environment covariation models have been proposed as explanations. High-risk (HR) designs that explore whether obstetric complications differentially predict outcomes in offspring at low risk (LR) vs HR for schizophrenia, while accounting for differences in rates of maternal risk factors, may shed light on this question. This study used prospectively obtained data to examine relationships between LBW and hypoxia on school outcome at age 15-16 years in a Finnish sample of 1070 offspring at LR for schizophrenia and 373 offspring at HR for schizophrenia, based on parental psychiatric history. Controlling for offspring sex, maternal smoking, social support, parity, age, and number of prenatal care visits, HR offspring performed worse than LR offspring across academic, nonacademic, and physical education domains. LBW predicted poorer academic and physical education performance in HR offspring, but not in LR offspring, and this association was similar for offspring of fathers vs mothers with schizophrenia. Hypoxia predicted poorer physical education score across risk groups. Rates of LBW and hypoxia were similar for LR and HR offspring and for offspring of fathers vs mothers with schizophrenia. Results support the hypothesis that genetic susceptibility to schizophrenia confers augmented vulnerability of the developing brain to the effects of obstetric complications, possibly via epigenetic mechanisms.
Transcription-based prediction of response to IFNbeta using supervised computational methods.

Directory of Open Access Journals (Sweden)

Sergio E Baranzini

2005-01-01

Full Text Available Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s. Recombinant human interferon beta (rIFNbeta is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.
Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

Science.gov (United States)

Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

2017-05-01

A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.
Inductive matrix completion for predicting gene-disease associations.

Science.gov (United States)

Natarajan, Nagarajan; Dhillon, Inderjit S

2014-06-15

Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.
A seven-gene CpG-island methylation panel predicts breast cancer progression

International Nuclear Information System (INIS)

Li, Yan; Melnikov, Anatoliy A.; Levenson, Victor; Guerra, Emanuela; Simeone, Pasquale; Alberti, Saverio; Deng, Youping

2015-01-01

DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors. CpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes. Seven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression. Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings. The online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary
Degradation Rate of 5-Fluorouracil in Metastatic Colorectal Cancer: A New Predictive Outcome Biomarker?

Directory of Open Access Journals (Sweden)

Andrea Botticelli

Full Text Available 5-FU based chemotherapy is the most common first line regimen used for metastatic colorectal cancer (mCRC. Identification of predictive markers of response to chemotherapy is a challenging approach for drug selection. The present study analyzes the predictive role of 5-FU degradation rate (5-FUDR and genetic polymorphisms (MTHFR, TSER, DPYD on survival.Genetic polymorphisms of MTHFR, TSER and DPYD, and the 5-FUDR of homogenous patients with mCRC were retrospectively studied. Genetic markers and the 5-FUDR were correlated with clinical outcome.133 patients affected by mCRC, treated with fluoropyrimidine-based chemotherapy from 2009 to 2014, were evaluated. Patients were classified into three metabolic classes, according to normal distribution of 5-FUDR in more than 1000 patients, as previously published: poor-metabolizer (PM with 5-FU-DR ≤ 0,85 ng/ml/106 cells/min (8 pts; normal metabolizer with 0,85 < 5-FU-DR < 2,2 ng/ml/106 cells/min (119 pts; ultra-rapid metabolizer (UM with 5-FU-DR ≥ 2,2 ng/ml/106 cells/min (6 pts. PM and UM groups showed a longer PFS respect to normal metabolizer group (14.5 and 11 months respectively vs 8 months; p = 0.029. A higher G3-4 toxicity rate was observed in PM and UM, respect to normal metabolizer (50% in both PM and UM vs 18%; p = 0.019. No significant associations between genes polymorphisms and outcomes or toxicities were observed.5-FUDR seems to be significantly involved in predicting survival of patients who underwent 5-FU based CHT for mCRC. Although our findings require confirmation in large prospective studies, they reinforce the concept that individual genetic variation may allow personalized selection of chemotherapy to optimize clinical outcomes.
Combining many interaction networks to predict gene function and analyze gene lists.

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Mostafavi, Sara; Morris, Quaid

2012-05-01

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting.

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Lakshmana Ayaru

Full Text Available There are no widely used models in clinical care to predict outcome in acute lower gastro-intestinal bleeding (ALGIB. If available these could help triage patients at presentation to appropriate levels of care/intervention and improve medical resource utilisation. We aimed to apply a state-of-the-art machine learning classifier, gradient boosting (GB, to predict outcome in ALGIB using non-endoscopic measurements as predictors.Non-endoscopic variables from patients with ALGIB attending the emergency departments of two teaching hospitals were analysed retrospectively for training/internal validation (n=170 and external validation (n=130 of the GB model. The performance of the GB algorithm in predicting recurrent bleeding, clinical intervention and severe bleeding was compared to a multiple logic regression (MLR model and two published MLR-based prediction algorithms (BLEED and Strate prediction rule.The GB algorithm had the best negative predictive values for the chosen outcomes (>88%. On internal validation the accuracy of the GB algorithm for predicting recurrent bleeding, therapeutic intervention and severe bleeding were (88%, 88% and 78% respectively and superior to the BLEED classification (64%, 68% and 63%, Strate prediction rule (78%, 78%, 67% and conventional MLR (74%, 74% 62%. On external validation the accuracy was similar to conventional MLR for recurrent bleeding (88% vs. 83% and therapeutic intervention (91% vs. 87% but superior for severe bleeding (83% vs. 71%.The gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models. These may be useful for risk stratification of patients on presentation to the emergency department.
Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

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Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

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Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.
Identification of rat genes by TWINSCAN gene prediction, RT-PCR, and direct sequencing

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Wu, Jia Qian; Shteynberg, David; Arumugam, Manimozhiyan

2004-01-01

an alternative approach: reverse transcription-polymerase chain reaction (RT-PCR) and direct sequencing based on dual-genome de novo predictions from TWINSCAN. We tested 444 TWINSCAN-predicted rat genes that showed significant homology to known human genes implicated in disease but that were partially...... in the single-intron experiment. Spliced sequences were amplified in 46 cases (34%). We conclude that this procedure for elucidating gene structures with native cDNA sequences is cost-effective and will become even more so as it is further optimized.......The publication of a draft sequence of a third mammalian genome--that of the rat--suggests a need to rethink genome annotation. New mammalian sequences will not receive the kind of labor-intensive annotation efforts that are currently being devoted to human. In this paper, we demonstrate...
Global discriminative learning for higher-accuracy computational gene prediction.

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Axel Bernal

2007-03-01

Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.
An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

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Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.
Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor.

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Vasselli, James R; Shih, Joanna H; Iyengar, Shuba R; Maranchie, Jodi; Riss, Joseph; Worrell, Robert; Torres-Cabala, Carlos; Tabios, Ray; Mariotti, Andra; Stearman, Robert; Merino, Maria; Walther, McClellan M; Simon, Richard; Klausner, Richard D; Linehan, W Marston

2003-06-10

To identify potential molecular determinants of tumor biology and possible clinical outcomes, global gene-expression patterns were analyzed in the primary tumors of patients with metastatic renal cell cancer by using cDNA microarrays. We used grossly dissected tumor masses that included tumor, blood vessels, connective tissue, and infiltrating immune cells to obtain a gene-expression "profile" from each primary tumor. Two patterns of gene expression were found within this uniformly staged patient population, which correlated with a significant difference in overall survival between the two patient groups. Subsets of genes most significantly associated with survival were defined, and vascular cell adhesion molecule-1 (VCAM-1) was the gene most predictive for survival. Therefore, despite the complex biological nature of metastatic cancer, basic clinical behavior as defined by survival may be determined by the gene-expression patterns expressed within the compilation of primary gross tumor cells. We conclude that survival in patients with metastatic renal cell cancer can be correlated with the expression of various genes based solely on the expression profile in the primary kidney tumor.
Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

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Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

2000-01-01

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society
Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

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Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

2000-09-18

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society.
Prediction of essential proteins based on subcellular localization and gene expression correlation.

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Fan, Yetian; Tang, Xiwei; Hu, Xiaohua; Wu, Wei; Ping, Qing

2017-12-01

Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction. The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction. In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.
Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

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Richard Danger

2018-01-01

Full Text Available Bronchiolitis obliterans syndrome (BOS, the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group, and 26 samples at or after BOS diagnosis (diagnosis group. An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group. We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1, T-cell leukemia/lymphoma protein 1A (TCL1A, and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01 and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS.
Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

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Mark Burton

2012-01-01

Full Text Available Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.
Predicting The Outcome of Marketing Negotiations: Role-Playing versus Unaided Opinions

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JS Armstrong; Philip D. Hutcherson

2005-01-01

Role -playing and unaided opinions were used to forecast the outcome of three negotiations. Consistent with prior re search, role-playing yielded more accurate predictions. In two studies on marketing negotiations, the predictions based on role-playing were correct for 53% of the predictions while unaided opinions were correct for only 7% (p
Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

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Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

2016-02-01

Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
Understanding reproducibility of human IVF traits to predict next IVF cycle outcome.

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Wu, Bin; Shi, Juanzi; Zhao, Wanqiu; Lu, Suzhen; Silva, Marta; Gelety, Timothy J

2014-10-01

Evaluating the failed IVF cycle often provides useful prognostic information. Before undergoing another attempt, patients experiencing an unsuccessful IVF cycle frequently request information about the probability of future success. Here, we introduced the concept of reproducibility and formulae to predict the next IVF cycle outcome. The experimental design was based on the retrospective review of IVF cycle data from 2006 to 2013 in two different IVF centers and statistical analysis. The reproducibility coefficients (r) of IVF traits including number of oocytes retrieved, oocyte maturity, fertilization, embryo quality and pregnancy were estimated using the interclass correlation coefficient between the repeated IVF cycle measurements for the same patient by variance component analysis. The formulae were designed to predict next IVF cycle outcome. The number of oocytes retrieved from patients and their fertilization rate had the highest reproducibility coefficients (r = 0.81 ~ 0.84), which indicated a very close correlation between the first retrieval cycle and subsequent IVF cycles. Oocyte maturity and number of top quality embryos had middle level reproducibility (r = 0.38 ~ 0.76) and pregnancy rate had a relative lower reproducibility (r = 0.23 ~ 0.27). Based on these parameters, the next outcome for these IVF traits might be accurately predicted by the designed formulae. The introduction of the concept of reproducibility to our human IVF program allows us to predict future IVF cycle outcomes. The traits of oocyte numbers retrieved, oocyte maturity, fertilization, and top quality embryos had higher or middle reproducibility, which provides a basis for accurate prediction of future IVF outcomes. Based on this prediction, physicians may counsel their patients or change patient's stimulation plans, and laboratory embryologists may improve their IVF techniques accordingly.
Ecological transition predictably associated with gene degeneration.

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Wessinger, Carolyn A; Rausher, Mark D

2015-02-01

Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Learned predictiveness and outcome predictability effects are not simply two sides of the same coin.

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Thorwart, Anna; Livesey, Evan J; Wilhelm, Francisco; Liu, Wei; Lachnit, Harald

2017-10-01

The Learned Predictiveness effect refers to the observation that learning about the relationship between a cue and an outcome is influenced by the predictive relevance of the cue for other outcomes. Similarly, the Outcome Predictability effect refers to a recent observation that the previous predictability of an outcome affects learning about this outcome in new situations, too. We hypothesize that both effects may be two manifestations of the same phenomenon and stimuli that have been involved in highly predictive relationships may be learned about faster when they are involved in new relationships regardless of their functional role in predictive learning as cues and outcomes. Four experiments manipulated both the relationships and the function of the stimuli. While we were able to replicate the standard effects, they did not survive a transfer to situations where the functional role of the stimuli changed, that is the outcome of the first phase becomes a cue in the second learning phase or the cue of the first phase becomes the outcome of the second phase. Furthermore, unlike learned predictiveness, there was little indication that the distribution of overt attention in the second phase was influenced by previous predictability. The results suggest that these 2 very similar effects are not manifestations of a more general phenomenon but rather independent from each other. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Predictability of Genetic Interactions from Functional Gene Modules

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Jonathan H. Young

2017-02-01

Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.
Attendance Rates in a Workplace Predict Subsequent Outcome of Employment-Based Reinforcement of Cocaine Abstinence in Methadone Patients

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Donlin, Wendy D.; Knealing, Todd W.; Needham, Mick; Wong, Conrad J.; Silverman, Kenneth

2008-01-01

This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N = 111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction…
Early Adolescent Affect Predicts Later Life Outcomes.

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Kansky, Jessica; Allen, Joseph P; Diener, Ed

2016-07-01

Subjective well-being as a predictor for later behavior and health has highlighted its relationship to health, work performance, and social relationships. However, the majority of such studies neglect the developmental nature of well-being in contributing to important changes across the transition to adulthood. To examine the potential role of subjective well-being as a long-term predictor of critical life outcomes, we examined indicators of positive and negative affect at age 14 as predictors of relationship, adjustment, self-worth, and career outcomes a decade later at ages 23 to 25, controlling for family income and gender. We utilised multi-informant methods including reports from the target participant, close friends, and romantic partners in a demographically diverse community sample of 184 participants. Early adolescent positive affect predicted fewer relationship problems (less self-reported and partner-reported conflict, and greater friendship attachment as rated by close peers) and healthy adjustment to adulthood (lower levels of depression, anxiety, and loneliness). It also predicted positive work functioning (higher levels of career satisfaction and job competence) and increased self-worth. Negative affect did not significantly predict any of these important life outcomes. In addition to predicting desirable mean levels of later outcomes, early positive affect predicted beneficial changes across time in many outcomes. The findings extend early research on the beneficial outcomes of subjective well-being by having an earlier assessment of well-being, including informant reports in measuring a large variety of outcome variables, and by extending the findings to a lower socioeconomic group of a diverse and younger sample. The results highlight the importance of considering positive affect as an important component of subjective well-being distinct from negative affect. © 2016 The International Association of Applied Psychology.
A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

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Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.
Predicting Hydrologic Function With Aquatic Gene Fragments

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Good, S. P.; URycki, D. R.; Crump, B. C.

2018-03-01

Recent advances in microbiology techniques, such as genetic sequencing, allow for rapid and cost-effective collection of large quantities of genetic information carried within water samples. Here we posit that the unique composition of aquatic DNA material within a water sample contains relevant information about hydrologic function at multiple temporal scales. In this study, machine learning was used to develop discharge prediction models trained on the relative abundance of bacterial taxa classified into operational taxonomic units (OTUs) based on 16S rRNA gene sequences from six large arctic rivers. We term this approach "genohydrology," and show that OTU relative abundances can be used to predict river discharge at monthly and longer timescales. Based on a single DNA sample from each river, the average Nash-Sutcliffe efficiency (NSE) for predicted mean monthly discharge values throughout the year was 0.84, while the NSE for predicted discharge values across different return intervals was 0.67. These are considerable improvements over predictions based only on the area-scaled mean specific discharge of five similar rivers, which had average NSE values of 0.64 and -0.32 for seasonal and recurrence interval discharge values, respectively. The genohydrology approach demonstrates that genetic diversity within the aquatic microbiome is a large and underutilized data resource with benefits for prediction of hydrologic function.

A comparative analysis of soft computing techniques for gene prediction.

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Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.
Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells.

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Hiroto Sasaki

Full Text Available Precise quantification of cellular potential of stem cells, such as human bone marrow-derived mesenchymal stem cells (hBMSCs, is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1 the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2 predictions of potentials are generated before differentiation cultures are initiated; (3 prediction of multiple potentials can be provided simultaneously for each sample; and (4 predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21 and cytoskeleton-related genes (PTK2, CD146, and CD49 already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature
Macaques can predict social outcomes from facial expressions.

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Waller, Bridget M; Whitehouse, Jamie; Micheletta, Jérôme

2016-09-01

There is widespread acceptance that facial expressions are useful in social interactions, but empirical demonstration of their adaptive function has remained elusive. Here, we investigated whether macaques can use the facial expressions of others to predict the future outcomes of social interaction. Crested macaques (Macaca nigra) were shown an approach between two unknown individuals on a touchscreen and were required to choose between one of two potential social outcomes. The facial expressions of the actors were manipulated in the last frame of the video. One subject reached the experimental stage and accurately predicted different social outcomes depending on which facial expressions the actors displayed. The bared-teeth display (homologue of the human smile) was most strongly associated with predicted friendly outcomes. Contrary to our predictions, screams and threat faces were not associated more with conflict outcomes. Overall, therefore, the presence of any facial expression (compared to neutral) caused the subject to choose friendly outcomes more than negative outcomes. Facial expression in general, therefore, indicated a reduced likelihood of social conflict. The findings dispute traditional theories that view expressions only as indicators of present emotion and instead suggest that expressions form part of complex social interactions where individuals think beyond the present.
Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.

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Tang, Zaixiang; Shen, Yueping; Li, Yan; Zhang, Xinyan; Wen, Jia; Qian, Chen'ao; Zhuang, Wenzhuo; Shi, Xinghua; Yi, Nengjun

2018-03-15

Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). nyi@uab.edu. Supplementary data are available at Bioinformatics online.
Gene expression signatures predict outcome in non-muscle invasive bladder carcinoma - a multi-center validation study

DEFF Research Database (Denmark)

Andersen, Lars Dyrskjøt; Zieger, Karsten; Real, Francisco X.

2007-01-01

and carcinoma in situ (CIS) and for predicting disease recurrence and progression. EXPERIMENTAL DESIGN: We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain, and France using custom microarrays. Molecular classifications were compared with pathologic....... CONCLUSION: This multicenter validation study confirms in an independent series the clinical utility of molecular classifiers to predict the outcome of patients initially diagnosed with non-muscle-invasive bladder cancer. This information may be useful to better guide patient treatment....
Do Patient Characteristics Predict Outcome of Psychodynamic Psychotherapy for Social Anxiety Disorder?

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Jörg Wiltink

Full Text Available Little is known about patient characteristics as predictors for outcome in manualized short term psychodynamic psychotherapy (PDT. No study has addressed which patient variables predict outcome of PDT for social anxiety disorder.In the largest multicenter trial on psychotherapy of social anxiety (SA to date comparing cognitive therapy, PDT and wait list condition N = 230 patients were assigned to receive PDT, of which N = 166 completed treatment. Treatment outcome was assessed based on diverse parameters such as endstate functioning, remission, response, and drop-out. The relationship between patient characteristics (demographic variables, mental co-morbidity, personality, interpersonal problems and outcome was analysed using logistic and linear regressions.Pre-treatment SA predicted up to 39 percent of variance of outcome. Only few additional baseline characteristics predicted better treatment outcome (namely, lower comorbidity and interpersonal problems with a limited proportion of incremental variance (5.5 to 10 percent, while, e.g., shame, self-esteem or harm avoidance did not.We argue that the central importance of pre-treatment symptom severity for predicting outcomes should advocate alternative treatment strategies (e.g. longer treatments, combination of psychotherapy and medication in those who are most disturbed. Given the relatively small amount of variance explained by the other patient characteristics, process variables and patient-therapist interaction should additionally be taken into account in future research.Controlled-trials.com/ISRCTN53517394.
MO-DE-207B-05: Predicting Gene Mutations in Renal Cell Carcinoma Based On CT Imaging Features: Validation Using TCGA-TCIA Datasets

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Chen, X; Zhou, Z; Thomas, K; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: The goal of this work is to investigate the use of contrast enhanced computed tomographic (CT) features for the prediction of mutations of BAP1, PBRM1, and VHL genes in renal cell carcinoma (RCC). Methods: For this study, we used two patient databases with renal cell carcinoma (RCC). The first one consisted of 33 patients from our institution (UT Southwestern Medical Center, UTSW). The second one consisted of 24 patients from the Cancer Imaging Archive (TCIA), where each patient is connected by a unique identi?er to the tissue samples from the Cancer Genome Atlas (TCGA). From the contrast enhanced CT image of each patient, tumor contour was first delineated by a physician. Geometry, intensity, and texture features were extracted from the delineated tumor. Based on UTSW dataset, we completed feature selection and trained a support vector machine (SVM) classifier to predict mutations of BAP1, PBRM1 and VHL genes. We then used TCIA-TCGA dataset to validate the predictive model build upon UTSW dataset. Results: The prediction accuracy of gene expression of TCIA-TCGA patients was 0.83 (20 of 24), 0.83 (20 of 24), and 0.75 (18 of 24) for BAP1, PBRM1, and VHL respectively. For BAP1 gene, texture feature was the most prominent feature type. For PBRM1 gene, intensity feature was the most prominent. For VHL gene, geometry, intensity, and texture features were all important. Conclusion: Using our feature selection strategy and models, we achieved predictive accuracy over 0.75 for all three genes under the condition of using patient data from one institution for training and data from other institutions for testing. These results suggest that radiogenomics can be used to aid in prognosis and used as convenient surrogates for expensive and time consuming gene assay procedures.
Predicting cellular growth from gene expression signatures.

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Edoardo M Airoldi

2009-01-01

Full Text Available Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.
A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks.

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Peng, Wei; Lan, Wei; Zhong, Jiancheng; Wang, Jianxin; Pan, Yi

2017-07-15

MicroRNAs have been reported to have close relationship with diseases due to their deregulation of the expression of target mRNAs. Detecting disease-related microRNAs is helpful for disease therapies. With the development of high throughput experimental techniques, a large number of microRNAs have been sequenced. However, it is still a big challenge to identify which microRNAs are related to diseases. Recently, researchers are interesting in combining multiple-biological information to identify the associations between microRNAs and diseases. In this work, we have proposed a novel method to predict the microRNA-disease associations based on four biological properties. They are microRNA, disease, gene and environment factor. Compared with previous methods, our method makes predictions not only by using the prior knowledge of associations among microRNAs, disease, environment factors and genes, but also by using the internal relationship among these biological properties. We constructed four biological networks based on the similarity of microRNAs, diseases, environment factors and genes, respectively. Then random walking was implemented on the four networks unequally. In the walking course, the associations can be inferred from the neighbors in the same networks. Meanwhile the association information can be transferred from one network to another. The results of experiment showed that our method achieved better prediction performance than other existing state-of-the-art methods. Copyright © 2017 Elsevier Inc. All rights reserved.
MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

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Yang Yi-Fan

2007-03-01

Full Text Available Abstract Background Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. Results This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs and Translation Initiation Sites (TISs. The former is based on a linguistic "Entropy Density Profile" (EDP model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Conclusion Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.
Neonatal Pulmonary MRI of Bronchopulmonary Dysplasia Predicts Short-term Clinical Outcomes.

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Higano, Nara S; Spielberg, David R; Fleck, Robert J; Schapiro, Andrew H; Walkup, Laura L; Hahn, Andrew D; Tkach, Jean A; Kingma, Paul S; Merhar, Stephanie L; Fain, Sean B; Woods, Jason C

2018-05-23

Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current NICHD/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. Forty-two neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD controls; 40±3 weeks post-menstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo-time and gradient echo) in a NICU-sited, neonatal-sized 1.5T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared to clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at NICU discharge (P<0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this non-ionizing technique can be implemented to phenotype disease and has potential to serially assess efficacy of individualized therapies.
Can We Predict Functional Outcome in Neonates with Hypoxic Ischemic Encephalopathy by the Combination of Neuroimaging and Electroencephalography?

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Nanavati, Tania; Seemaladinne, Nirupama; Regier, Michael; Yossuck, Panitan; Pergami, Paola

2015-01-01

Background Neonatal hypoxic ischemic encephalopathy (HIE) is a major cause of mortality, morbidity, and long-term neurological deficits. Despite the availability of neuroimaging and neurophysiological testing, tools for accurate early diagnosis and prediction of developmental outcome are still lacking. The goal of this study was to determine if combined use of magnetic resonance imaging (MRI) and electroencephalography (EEG) findings could support outcome prediction. Methods We retrospectively reviewed records of 17 HIE neonates, classified brain MRI and EEG findings based on severity, and assessed clinical outcome up to 48 months. We determined the relation between MRI/EEG findings and clinical outcome. Results We demonstrated a significant relationship between MRI findings and clinical outcome (Fisher’s exact test, p = 0.017). EEG provided no additional information about the outcome beyond that contained in the MRI score. The statistical model for outcome prediction based on random forests suggested that EEG readings at 24 hours and 72 hours could be important variables for outcome prediction, but this needs to be investigated further. Conclusion Caution should be used when discussing prognosis for neonates with mild-to-moderate HIE based on early MR imaging and EEG findings. A robust, quantitative marker of HIE severity that allows for accurate prediction of long-term outcome, particularly for mild-to-moderate cases, is still needed. PMID:25862075
Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.

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Quan Li

Full Text Available The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.
A network approach to predict pathogenic genes for Fusarium graminearum.

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Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

2010-10-04

Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which
A network approach to predict pathogenic genes for Fusarium graminearum.

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Xiaoping Liu

Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other
Developing a risk prediction model for the functional outcome after hip arthroscopy.

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Stephan, Patrick; Röling, Maarten A; Mathijssen, Nina M C; Hannink, Gerjon; Bloem, Rolf M

2018-04-19

Hip arthroscopic treatment is not equally beneficial for every patient undergoing this procedure. Therefore, the purpose of this study was to develop a clinical prediction model for functional outcome after surgery based on preoperative factors. Prospective data was collected on a cohort of 205 patients having undergone hip arthroscopy between 2011 and 2015. Demographic and clinical variables and patient reported outcome (PRO) scores were collected, and considered as potential predictors. Successful outcome was defined as either a Hip Outcome Score (HOS)-ADL score of over 80% or improvement of 23%, defined by the minimal clinical important difference, 1 year after surgery. The prediction model was developed using backward logistic regression. Regression coefficients were converted into an easy to use prediction rule. The analysis included 203 patients, of which 74% had a successful outcome. Female gender (OR: 0.37 (95% CI 0.17-0.83); p = 0.02), pincer impingement (OR: 0.47 (95% CI 0.21-1.09); p = 0.08), labral tear (OR: 0.46 (95% CI 0.20-1.06); p = 0.07), HOS-ADL score (IQR OR: 2.01 (95% CI 0.99-4.08); p = 0.05), WHOQOL physical (IQR OR: 0.43 (95% CI 0.22-0.87); p = 0.02) and WHOQOL psychological (IQR OR: 2.40 (95% CI 1.38-4.18); p = prediction model of successful functional outcome 1 year after hip arthroscopy. The model's discriminating accuracy turned out to be fair, as 71% (95% CI: 64-80%) of the patients were classified correctly. The developed prediction model can predict the functional outcome of patients that are considered for a hip arthroscopic intervention, containing six easy accessible preoperative risk factors. The model can be further improved trough external validation and/or adding additional potential predictors.
Exceptions to the rule: case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes.

Science.gov (United States)

Rosenthal, E T; Bowles, K R; Pruss, D; van Kan, A; Vail, P J; McElroy, H; Wenstrup, R J

2015-12-01

Based on current consensus guidelines and standard practice, many genetic variants detected in clinical testing are classified as disease causing based on their predicted impact on the normal expression or function of the gene in the absence of additional data. However, our laboratory has identified a subset of such variants in hereditary cancer genes for which compelling contradictory evidence emerged after the initial evaluation following the first observation of the variant. Three representative examples of variants in BRCA1, BRCA2 and MSH2 that are predicted to disrupt splicing, prematurely truncate the protein, or remove the start codon were evaluated for pathogenicity by analyzing clinical data with multiple classification algorithms. Available clinical data for all three variants contradicts the expected pathogenic classification. These variants illustrate potential pitfalls associated with standard approaches to variant classification as well as the challenges associated with monitoring data, updating classifications, and reporting potentially contradictory interpretations to the clinicians responsible for translating test outcomes to appropriate clinical action. It is important to address these challenges now as the model for clinical testing moves toward the use of large multi-gene panels and whole exome/genome analysis, which will dramatically increase the number of genetic variants identified. © 2015 The Authors. Clinical Genetics published by John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

Science.gov (United States)

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
Mortality and One-Year Functional Outcome in Elderly and Very Old Patients with Severe Traumatic Brain Injuries: Observed and Predicted

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Cecilie Røe

2015-01-01

Full Text Available The aim of the present study was to evaluate mortality and functional outcome in old and very old patients with severe traumatic brain injury (TBI and compare to the predicted outcome according to the internet based CRASH (Corticosteroid Randomization After Significant Head injury model based prediction, from the Medical Research Council (MRC. Methods. Prospective, national multicenter study including patients with severe TBI ≥65 years. Predicted mortality and outcome were calculated based on clinical information (CRASH basic (age, GCS score, and pupil reactivity to light, as well as with additional CT findings (CRASH CT. Observed 14-day mortality and favorable/unfavorable outcome according to the Glasgow Outcome Scale at one year was compared to the predicted outcome according to the CRASH models. Results. 97 patients, mean age 75 (SD 7 years, 64% men, were included. Two patients were lost to follow-up; 48 died within 14 days. The predicted versus the observed odds ratio (OR for mortality was 2.65. Unfavorable outcome (GOSE < 5 was observed at one year follow-up in 72% of patients. The CRASH models predicted unfavorable outcome in all patients. Conclusion. The CRASH model overestimated mortality and unfavorable outcome in old and very old Norwegian patients with severe TBI.
Mortality and One-Year Functional Outcome in Elderly and Very Old Patients with Severe Traumatic Brain Injuries: Observed and Predicted

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Røe, Cecilie; Skandsen, Toril; Manskow, Unn; Ader, Tiina; Anke, Audny

2015-01-01

The aim of the present study was to evaluate mortality and functional outcome in old and very old patients with severe traumatic brain injury (TBI) and compare to the predicted outcome according to the internet based CRASH (Corticosteroid Randomization After Significant Head injury) model based prediction, from the Medical Research Council (MRC). Methods. Prospective, national multicenter study including patients with severe TBI ≥65 years. Predicted mortality and outcome were calculated based on clinical information (CRASH basic) (age, GCS score, and pupil reactivity to light), as well as with additional CT findings (CRASH CT). Observed 14-day mortality and favorable/unfavorable outcome according to the Glasgow Outcome Scale at one year was compared to the predicted outcome according to the CRASH models. Results. 97 patients, mean age 75 (SD 7) years, 64% men, were included. Two patients were lost to follow-up; 48 died within 14 days. The predicted versus the observed odds ratio (OR) for mortality was 2.65. Unfavorable outcome (GOSE < 5) was observed at one year follow-up in 72% of patients. The CRASH models predicted unfavorable outcome in all patients. Conclusion. The CRASH model overestimated mortality and unfavorable outcome in old and very old Norwegian patients with severe TBI. PMID:26688614

Principles for guiding the ONKALO prediction-outcome studies

International Nuclear Information System (INIS)

Andersson, J.; Hudson, J.A.; Anttila, P.; Koskinen, L.; Pitkaenen, P.; Hautojaervi, A.; Wikstroem, L.

2005-09-01

This document provides the necessary foundation for establishing the strategy for the Prediction-Outcome studies currently being conducted by the ONKALO Modelling Task Force (OMTF) during the construction of the ONKALO ramp. These studies relate to the geology, rock mechanics, hydrogeology and hydrogeochemistry. The purpose of the Prediction-Outcome campaign currently underway in the ONKALO ramp tunnel is to optimize Posiva's ability to predict rock conditions ahead of the excavation face. The aim of the work is: to enhance confidence in ability to predict rock conditions in general - and especially for the repository volumes; (later) testing and verification of repository design rules as it would not be possible to make too many additional boreholes in repository volume; and to support the ongoing construction work and make possible the application of the CEIC method. The document also presents current plans for at what stages of the ONKALO construction predictions and outcome assessments will be made as well as current plans for what properties and impacts will be predicted. These plans will evidently be subject to revision during the course of the work. (orig.)
Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

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Mucaki, Eliseos J; Baranova, Katherina; Pham, Huy Q; Rezaeian, Iman; Angelov, Dimo; Ngom, Alioune; Rueda, Luis; Rogan, Peter K

2016-01-01

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes BCL2L1, BBC3, FGF2, FN1, and TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B ) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for
Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

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Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Differential cytokine gene expression according to outcome in a hamster model of leptospirosis.

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Frédérique Vernel-Pauillac

Full Text Available BACKGROUND: Parameters predicting the evolution of leptospirosis would be useful for clinicians, as well as to better understand severe leptospirosis, but are scarce and rarely validated. Because severe leptospirosis includes septic shock, similarities with predictors evidenced for sepsis and septic shock were studied in a hamster model. METHODOLOGY/PRINCIPAL FINDINGS: Using an LD50 model of leptospirosis in hamsters, we first determined that 3 days post-infection was a time-point that allowed studying the regulation of immune gene expression and represented the onset of the clinical signs of the disease. In the absence of tools to assess serum concentrations of immune effectors in hamsters, we determined mRNA levels of various immune genes, especially cytokines, together with leptospiraemia at this particular time-point. We found differential expression of both pro- and anti-inflammatory mediators, with significantly higher expression levels of tumor necrosis factor alpha, interleukin 1alpha, cyclo-oxygenase 2 and interleukin 10 genes in nonsurvivors compared to survivors. Higher leptospiraemia was also observed in nonsurvivors. Lastly, we demonstrated the relevance of these results by comparing their respective expression levels using a LD100 model or an isogenic high-passage nonvirulent variant. CONCLUSIONS/SIGNIFICANCE: Up-regulated gene expression of both pro- and anti-inflammatory immune effectors in hamsters with fatal outcome in an LD50 model of leptospirosis, together with a higher Leptospira burden, suggest that these gene expression levels could be predictors of adverse outcome in leptospirosis.
Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

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Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.
An ensemble method to predict target genes and pathways in uveal melanoma

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Wei Chao

2018-04-01

Full Text Available This work proposes to predict target genes and pathways for uveal melanoma (UM based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC, a causal inference method (IDA and a regression method (Lasso utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.
Literature-based condition-specific miRNA-mRNA target prediction.

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Minsik Oh

Full Text Available miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction
Probability-based collaborative filtering model for predicting gene–disease associations

OpenAIRE

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-01-01

Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...
Dinucleotide controlled null models for comparative RNA gene prediction.

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Gesell, Tanja; Washietl, Stefan

2008-05-27

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz
Dinucleotide controlled null models for comparative RNA gene prediction

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Gesell Tanja

2008-05-01

Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require
An evidence-based decision assistance model for predicting training outcome in juvenile guide dogs.

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Harvey, Naomi D; Craigon, Peter J; Blythe, Simon A; England, Gary C W; Asher, Lucy

2017-01-01

Working dog organisations, such as Guide Dogs, need to regularly assess the behaviour of the dogs they train. In this study we developed a questionnaire-style behaviour assessment completed by training supervisors of juvenile guide dogs aged 5, 8 and 12 months old (n = 1,401), and evaluated aspects of its reliability and validity. Specifically, internal reliability, temporal consistency, construct validity, predictive criterion validity (comparing against later training outcome) and concurrent criterion validity (comparing against a standardised behaviour test) were evaluated. Thirty-nine questions were sourced either from previously published literature or created to meet requirements identified via Guide Dogs staff surveys and staff feedback. Internal reliability analyses revealed seven reliable and interpretable trait scales named according to the questions within them as: Adaptability; Body Sensitivity; Distractibility; Excitability; General Anxiety; Trainability and Stair Anxiety. Intra-individual temporal consistency of the scale scores between 5-8, 8-12 and 5-12 months was high. All scales excepting Body Sensitivity showed some degree of concurrent criterion validity. Predictive criterion validity was supported for all seven scales, since associations were found with training outcome, at at-least one age. Thresholds of z-scores on the scales were identified that were able to distinguish later training outcome by identifying 8.4% of all dogs withdrawn for behaviour and 8.5% of all qualified dogs, with 84% and 85% specificity. The questionnaire assessment was reliable and could detect traits that are consistent within individuals over time, despite juvenile dogs undergoing development during the study period. By applying thresholds to scores produced from the questionnaire this assessment could prove to be a highly valuable decision-making tool for Guide Dogs. This is the first questionnaire-style assessment of juvenile dogs that has shown value in predicting
Computational prediction and experimental validation of Ciona intestinalis microRNA genes

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Pasquinelli Amy E

2007-11-01

Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.
Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

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Thomas H A Ederveen

Full Text Available Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4% and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.
Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility.

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Del Arco, Alberto; Park, Junchol; Wood, Jesse; Kim, Yunbok; Moghaddam, Bita

2017-08-30

The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action. SIGNIFICANCE STATEMENT Tracking action-outcome contingencies and modifying behavior when those contingencies change is critical to behavioral flexibility. We find that ensembles of dorsomedial prefrontal cortex neurons differentiate between expected outcomes when action-outcome contingencies change. This predictive mode of signaling may be used to promote a new response strategy at the service of behavioral flexibility. Copyright © 2017 the authors 0270-6474/17/378363-11$15.00/0.
Outcome-Driven Cluster Analysis with Application to Microarray Data.

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Jessie J Hsu

Full Text Available One goal of cluster analysis is to sort characteristics into groups (clusters so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes into groups of highly correlated genes that have the same effect on the outcome (recovery. We propose a random effects model where the genes within each group (cluster equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.
The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

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Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.
Cerebrospinal fluid neurofilament light chain levels predict visual outcome after optic neuritis

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Modvig, Signe; Degn, M; Sander, B

2016-01-01

BACKGROUND: Optic neuritis is a good model for multiple sclerosis relapse, but currently no tests can accurately predict visual outcome. OBJECTIVE: The purpose of this study was to examine whether cerebrospinal fluid (CSF) biomarkers of tissue damage and remodelling (neurofilament light chain (NF......-L, β=-1.1, p=0.0150 for GC-IPL). Complete/incomplete remission was determined based on LCVA from 30 healthy controls. NF-L had a positive predictive value of 91% and an area under the curve (AUC) of 0.79 for incomplete remission. CONCLUSION: CSF NF-L is a promising biomarker of visual outcome after...
Preoperative prediction model of outcome after cholecystectomy for symptomatic gallstones

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Borly, L; Anderson, I B; Bardram, L

1999-01-01

and sonography evaluated gallbladder motility, gallstones, and gallbladder volume. Preoperative variables in patients with or without postcholecystectomy pain were compared statistically, and significant variables were combined in a logistic regression model to predict the postoperative outcome. RESULTS: Eighty...... and by the absence of 'agonizing' pain and of symptoms coinciding with pain (P model 15 of 18 predicted patients had postoperative pain (PVpos = 0.83). Of 62 patients predicted as having no pain postoperatively, 56 were pain-free (PVneg = 0.90). Overall accuracy...... was 89%. CONCLUSION: From this prospective study a model based on preoperative symptoms was developed to predict postcholecystectomy pain. Since intrastudy reclassification may give too optimistic results, the model should be validated in future studies....
Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

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Hsu Hui-Chi

2011-04-01

Full Text Available Abstract Background Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. Methods Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. Results We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was
Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

International Nuclear Information System (INIS)

Kao, Kuo-Jang; Chang, Kai-Ming; Hsu, Hui-Chi; Huang, Andrew T

2011-01-01

Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic

Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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Chen Xin

2012-10-01

Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable
Gene prediction in metagenomic fragments: A large scale machine learning approach

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Morgenstern Burkhard

2008-04-01

Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene
Pairagon+N-SCAN_EST: a model-based gene annotation pipeline

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Arumugam, Manimozhiyan; Wei, Chaochun; Brown, Randall H

2006-01-01

This paper describes Pairagon+N-SCAN_EST, a gene annotation pipeline that uses only native alignments. For each expressed sequence it chooses the best genomic alignment. Systems like ENSEMBL and ExoGean rely on trans alignments, in which expressed sequences are aligned to the genomic loci...... with de novo gene prediction by using N-SCAN_EST. N-SCAN_EST is based on a generalized HMM probability model augmented with a phylogenetic conservation model and EST alignments. It can predict complete transcripts by extending or merging EST alignments, but it can also predict genes in regions without EST...
Prediction of surgical outcome in compressive cervical myelopathy: A novel clinicoradiological prognostic score

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Rishi Anil Aggarwal

2016-01-01

, following surgery in CCM. A simple scoring system based on clinicoradiological parameters is suggested in this paper to predict the outcome following surgery in cases of CCM.
Gene expression alterations associated with outcome in aromatase inhibitor-treated ER+ early-stage breast cancer patients

DEFF Research Database (Denmark)

Gravgaard Thomsen, Karina Hedelund; Lyng, Maria Bibi; Elias, Daniel

2015-01-01

predictive of outcome of ER+ breast cancer patients treated with AIs are needed. Global gene expression analysis was performed on ER+ primary breast cancers from patients treated with adjuvant AI monotherapy; half experienced recurrence (median follow-up 6.7 years). Gene expression alterations were validated...... by qRT-PCR, and functional studies evaluating the effect of siRNA-mediated gene knockdown on cell growth were performed. Twenty-six genes, including TFF3, DACH1, RGS5, and GHR, were shown to exhibit altered expression in tumors from patients with recurrence versus non-recurrent (fold change ≥1.5, p ....05), and the gene expression alterations were confirmed using qRT-PCR. Ten of these 26 genes could be linked in a network associated with cellular proliferation, growth, and development. TFF3, which encodes for trefoil factor 3 and is an estrogen-responsive oncogene shown to play a functional role in tamoxifen...
Has growth mixture modeling improved our understanding of how early change predicts psychotherapy outcome?

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Koffmann, Andrew

2017-03-02

Early change in psychotherapy predicts outcome. Seven studies have used growth mixture modeling [GMM; Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class-latent growth modeling. In L. M. Collins & A. G. Sawyers (Eds.), New methods for the analysis of change (pp. 291-322). Washington, DC: American Psychological Association] to identify patient classes based on early change but have yielded conflicting results. Here, we review the earlier studies and apply GMM to a new data set. In a university-based training clinic, 251 patients were administered the Outcome Questionnaire-45 [Lambert, M. J., Hansen, N. B., Umphress, V., Lunnen, K., Okiishi, J., Burlingame, G., … Reisinger, C. W. (1996). Administration and scoring manual for the Outcome Questionnaire (OQ 45.2). Wilmington, DE: American Professional Credentialing Services] at each psychotherapy session. We used GMM to identify class structure based on change in the first six sessions and examined trajectories as predictors of outcome. The sample was best described as a single class. There was no evidence of autoregressive trends in the data. We achieved better fit to the data by permitting latent variables some degree of kurtosis, rather than to assume multivariate normality. Treatment outcome was predicted by the amount of early improvement, regardless of initial level of distress. The presence of sudden early gains or losses did not further improve outcome prediction. Early improvement is an easily computed, powerful predictor of psychotherapy outcome. The use of GMM to investigate the relationship between change and outcome is technically complex and computationally intensive. To date, it has not been particularly informative.
Reduction in WT1 gene expression during early treatment predicts the outcome in patients with acute myeloid leukemia.

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Andersson, Charlotta; Li, Xingru; Lorenz, Fryderyk; Golovleva, Irina; Wahlin, Anders; Li, Aihong

2012-12-01

Wilms tumor gene 1 (WT1) expression has been suggested as an applicable minimal residual disease marker in acute myeloid leukemia (AML). We evaluated the use of this marker in 43 adult AML patients. Quantitative assessment of WT1 gene transcripts was performed using real-time quantitative-polymerase chain reaction assay. Samples from both the peripheral blood and the bone marrow were analyzed at diagnosis and during follow-up. A strong correlation was observed between WT1 normalized with 2 different control genes (β-actin and ABL1, P0.05). A≥1-log reduction in WT1 expression in bone marrow samples taken freedom from relapse (P=0.010) when β-actin was used as control gene. Furthermore, a reduction in WT1 expression by ≥2 logs in peripheral blood samples taken at a later time point significantly correlated with a better outcome for overall survival (P=0.004) and freedom from relapse (P=0.012). This result was achieved when normalizing against both β-actin and ABL1. These results therefore suggest that WT1 gene expression can provide useful information for minimal residual disease detection in adult AML patients and that combined use of control genes can give more informative results.
Cluster analysis as a prediction tool for pregnancy outcomes.

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Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

2015-03-01

Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.
Attendance Rates in A Workplace Predict Subsequent Outcome of Employment-Based Reinforcement of Cocaine Abstinence in Methadone Patients

OpenAIRE

Donlin, Wendy D; Knealing, Todd W; Needham, Mick; Wong, Conrad J; Silverman, Kenneth

2008-01-01

This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N = 111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction period, participants provided urine samples but could work independent of their urinalysis results. After the induction period, participants had to prov...
AUC-based biomarker ensemble with an application on gene scores predicting low bone mineral density.

Science.gov (United States)

Zhao, X G; Dai, W; Li, Y; Tian, L

2011-11-01

The area under the receiver operating characteristic (ROC) curve (AUC), long regarded as a 'golden' measure for the predictiveness of a continuous score, has propelled the need to develop AUC-based predictors. However, the AUC-based ensemble methods are rather scant, largely due to the fact that the associated objective function is neither continuous nor concave. Indeed, there is no reliable numerical algorithm identifying optimal combination of a set of biomarkers to maximize the AUC, especially when the number of biomarkers is large. We have proposed a novel AUC-based statistical ensemble methods for combining multiple biomarkers to differentiate a binary response of interest. Specifically, we propose to replace the non-continuous and non-convex AUC objective function by a convex surrogate loss function, whose minimizer can be efficiently identified. With the established framework, the lasso and other regularization techniques enable feature selections. Extensive simulations have demonstrated the superiority of the new methods to the existing methods. The proposal has been applied to a gene expression dataset to construct gene expression scores to differentiate elderly women with low bone mineral density (BMD) and those with normal BMD. The AUCs of the resulting scores in the independent test dataset has been satisfactory. Aiming for directly maximizing AUC, the proposed AUC-based ensemble method provides an efficient means of generating a stable combination of multiple biomarkers, which is especially useful under the high-dimensional settings. lutian@stanford.edu. Supplementary data are available at Bioinformatics online.
Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.

Directory of Open Access Journals (Sweden)

Rubén Armañanzas

Full Text Available Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE. Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.
A genome-wide gene function prediction resource for Drosophila melanogaster.

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Han Yan

2010-08-01

Full Text Available Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.
Collateral flow predicts outcome after basilar artery occlusion : The posterior circulation collateral score

NARCIS (Netherlands)

van der Hoeven, Erik J R J; McVerry, Ferghal; Vos, Jan Albert; Algra, Ale; Puetz, Volker; Kappelle, L. Jaap; Schonewille, Wouter J.

2016-01-01

BACKGROUND AND AIM: Our aim was to assess the prognostic value of a semiquantitative computed tomography angiography-based grading system, for the prediction of outcome in patients with acute basilar artery occlusion, based on the presence of potential collateral pathways on computed tomography
Paired hormone response elements predict caveolin-1 as a glucocorticoid target gene.

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Marinus F van Batenburg

2010-01-01

Full Text Available Glucocorticoids act in part via glucocorticoid receptor binding to hormone response elements (HREs, but their direct target genes in vivo are still largely unknown. We developed the criterion that genomic occurrence of paired HREs at an inter-HRE distance less than 200 bp predicts hormone responsiveness, based on synergy of multiple HREs, and HRE information from known target genes. This criterion predicts a substantial number of novel responsive genes, when applied to genomic regions 10 kb upstream of genes. Multiple-tissue in situ hybridization showed that mRNA expression of 6 out of 10 selected genes was induced in a tissue-specific manner in mice treated with a single dose of corticosterone, with the spleen being the most responsive organ. Caveolin-1 was strongly responsive in several organs, and the HRE pair in its upstream region showed increased occupancy by glucocorticoid receptor in response to corticosterone. Our approach allowed for discovery of novel tissue specific glucocorticoid target genes, which may exemplify responses underlying the permissive actions of glucocorticoids.
Identification of Novel Signal Transduction, Immune Function, and Oxidative Stress Genes and Pathways by Topiramate for Treatment of Methamphetamine Dependence Based on Secondary Outcomes

Directory of Open Access Journals (Sweden)

Tianhua Niu

2017-12-01

Full Text Available BackgroundTopiramate (TPM is suggested to be a promising medication for treatment of methamphetamine (METH dependence, but the molecular basis remains to be elucidated.MethodsAmong 140 METH-dependent participants randomly assigned to receive either TPM (N = 69 or placebo (N = 71 in a previously conducted randomized controlled trial, 50 TPM- and 49 placebo-treated participants had a total 212 RNA samples available at baseline, week 8, and week 12 time points. Following our primary analysis of gene expression data, we reanalyzed the microarray expression data based on a latent class analysis of binary secondary outcomes during weeks 1–12 that provided a classification of 21 responders and 31 non-responders with consistent responses at both time points.ResultsBased on secondary outcomes, 1,381, 576, 905, and 711 differentially expressed genes at nominal P values < 0.05 were identified in responders versus non-responders for week 8 TPM, week 8 placebo, week 12 TPM, and week 12 placebo groups, respectively. Among 1,381 genes identified in week 8 TPM responders, 359 genes were identified in both week 8 and week 12 TPM groups, of which 300 genes were exclusively detected in TPM responders. Of them, 32 genes had nominal P values < 5 × 10−3 at either week 8 or week 12 and false discovery rates < 0.15 at both time points with consistent directions of gene expression changes, which include GABARAPL1, GPR155, and IL15RA in GABA receptor signaling that represent direct targets for TPM. Analyses of these 300 genes revealed 7 enriched pathways belonging to neuronal function/synaptic plasticity, signal transduction, inflammation/immune function, and oxidative stress response categories. No pathways were enriched for 72 genes exclusively detected in both week 8 and week 12 placebo groups.ConclusionThis secondary analysis study of gene expression data from a TPM clinical trial not only yielded consistent results with those of primary
Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

Science.gov (United States)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC) = 0.65 (p = 0.004), 0.73 (p = 0.026), and 0.66 (p = 0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC = 0.68 (p = 0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC = 0.60 (p = 0.092) and 0.65 (p = 0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained
Gene expression profiles in stages II and III colon cancers

DEFF Research Database (Denmark)

Thorsteinsson, Morten; Kirkeby, Lene T; Hansen, Raino

2012-01-01

PURPOSE: A 128-gene signature has been proposed to predict outcome in patients with stages II and III colorectal cancers. In the present study, we aimed to reproduce and validate the 128-gene signature in external and independent material. METHODS: Gene expression data from the original material...... were retrieved from the Gene Expression Omnibus (GEO) (n¿=¿111) in addition to a Danish data set (n¿=¿37). All patients had stages II and III colon cancers. A Prediction Analysis of Microarray classifier, based on the 128-gene signature and the original training set of stage I (n¿=¿65) and stage IV (n...... correctly predicted as stage IV-like, and the remaining patients were predicted as stage I-like and unclassifiable, respectively. Stage II patients could not be stratified. CONCLUSIONS: The 128-gene signature showed reproducibility in stage III colon cancer, but could not predict recurrence in stage II...
Prediction of outcome in patients with low back pain

DEFF Research Database (Denmark)

Kongsted, Alice; Andersen, Cathrine Hedegaard; Mørk Hansen, Martin

2016-01-01

The clinical course of low back pain (LBP) cannot be accurately predicted by existing prediction tools. Therefore clinicians rely largely on their experience and clinical judgement. The objectives of this study were to investigate 1) which patient characteristics were associated with chiropractors...... intensity (0-10) and disability (RMDQ) after 2-weeks, 3-months, and 12-months. The course of LBP in 859 patients was predicted to be short (54%), prolonged (36%), or chronic (7%). Clinicians' expectations were most strongly associated with education, LBP history, radiating pain, and neurological signs......' expectations of outcome from a LBP episode, 2) if clinicians' expectations related to outcome, 3) how accurate clinical predictions were as compared to those of the STarT Back Screening Tool (SBT), and 4) if accuracy was improved by combining clinicians' expectations and the SBT. Outcomes were measured as LBP...
Hierarchy in gene expression is predictive of risk, progression, and outcome in adult acute myeloid leukemia

Science.gov (United States)

Tripathi, Shubham; Deem, Michael W.

2015-02-01

Cancer progresses with a change in the structure of the gene network in normal cells. We define a measure of organizational hierarchy in gene networks of affected cells in adult acute myeloid leukemia (AML) patients. With a retrospective cohort analysis based on the gene expression profiles of 116 AML patients, we find that the likelihood of future cancer relapse and the level of clinical risk are directly correlated with the level of organization in the cancer related gene network. We also explore the variation of the level of organization in the gene network with cancer progression. We find that this variation is non-monotonic, which implies the fitness landscape in the evolution of AML cancer cells is non-trivial. We further find that the hierarchy in gene expression at the time of diagnosis may be a useful biomarker in AML prognosis.
Hierarchy in gene expression is predictive of risk, progression, and outcome in adult acute myeloid leukemia

International Nuclear Information System (INIS)

Tripathi, Shubham; Deem, Michael W

2015-01-01

Cancer progresses with a change in the structure of the gene network in normal cells. We define a measure of organizational hierarchy in gene networks of affected cells in adult acute myeloid leukemia (AML) patients. With a retrospective cohort analysis based on the gene expression profiles of 116 AML patients, we find that the likelihood of future cancer relapse and the level of clinical risk are directly correlated with the level of organization in the cancer related gene network. We also explore the variation of the level of organization in the gene network with cancer progression. We find that this variation is non-monotonic, which implies the fitness landscape in the evolution of AML cancer cells is non-trivial. We further find that the hierarchy in gene expression at the time of diagnosis may be a useful biomarker in AML prognosis. (paper)

Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

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Sondag, Lotte; Ruijter, Barry J; Tjepkema-Cloostermans, Marleen C; Beishuizen, Albertus; Bosch, Frank H; van Til, Janine A; van Putten, Michel J A M; Hofmeijer, Jeannette

2017-05-15

We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.
Observational attachment theory-based parenting measures predict children's attachment narratives independently from social learning theory-based measures.

Science.gov (United States)

Matias, Carla; O'Connor, Thomas G; Futh, Annabel; Scott, Stephen

2014-01-01

Conceptually and methodologically distinct models exist for assessing quality of parent-child relationships, but few studies contrast competing models or assess their overlap in predicting developmental outcomes. Using observational methodology, the current study examined the distinctiveness of attachment theory-based and social learning theory-based measures of parenting in predicting two key measures of child adjustment: security of attachment narratives and social acceptance in peer nominations. A total of 113 5-6-year-old children from ethnically diverse families participated. Parent-child relationships were rated using standard paradigms. Measures derived from attachment theory included sensitive responding and mutuality; measures derived from social learning theory included positive attending, directives, and criticism. Child outcomes were independently-rated attachment narrative representations and peer nominations. Results indicated that Attachment theory-based and Social Learning theory-based measures were modestly correlated; nonetheless, parent-child mutuality predicted secure child attachment narratives independently of social learning theory-based measures; in contrast, criticism predicted peer-nominated fighting independently of attachment theory-based measures. In young children, there is some evidence that attachment theory-based measures may be particularly predictive of attachment narratives; however, no single model of measuring parent-child relationships is likely to best predict multiple developmental outcomes. Assessment in research and applied settings may benefit from integration of different theoretical and methodological paradigms.
A Polymorphism within the Vitamin D Transporter Gene Predicts Outcome in Metastatic Colorectal Cancer Patients Treated with FOLFIRI/Bevacizumab or FOLFIRI/Cetuximab.

Science.gov (United States)

Berger, Martin D; Stintzing, Sebastian; Heinemann, Volker; Cao, Shu; Yang, Dongyun; Sunakawa, Yu; Matsusaka, Satoshi; Ning, Yan; Okazaki, Satoshi; Miyamoto, Yuji; Suenaga, Mitsukuni; Schirripa, Marta; Hanna, Diana L; Soni, Shivani; Puccini, Alberto; Zhang, Wu; Cremolini, Chiara; Falcone, Alfredo; Loupakis, Fotios; Lenz, Heinz-Josef

2018-02-15

Purpose: Vitamin D exerts its inhibitory influence on colon cancer growth by inhibiting Wnt signaling and angiogenesis. We hypothesized that SNPs in genes involved in vitamin D transport, metabolism, and signaling are associated with outcome in metastatic colorectal cancer (mCRC) patients treated with first-line FOLFIRI and bevacizumab. Experimental Design: 522 mCRC patients enrolled in the FIRE-3 (discovery cohort) and TRIBE (validation set) trials treated with FOLFIRI/bevacizumab were included in this study. 278 patients receiving FOLFIRI and cetuximab (FIRE-3) served as a control cohort. Six SNPs in 6 genes ( GC, CYP24A1, CYP27B1, VDR, DKK1, CST5 ) were analyzed. Results: In the discovery cohort, AA carriers of the GC rs4588 SNP encoding for the vitamin D-binding protein, and treated with FOLFIRI/bevacizumab had a shorter overall survival (OS) than those harboring any C allele (15.9 vs. 25.1 months) in both univariable ( P = 0.001) and multivariable analyses ( P = 0.047). This association was confirmed in the validation cohort in multivariable analysis (OS 18.1 vs. 26.2 months, HR, 1.83; P = 0.037). Interestingly, AA carriers in the control set exhibited a longer OS (48.0 vs. 25.2 months, HR, 0.50; P = 0.021). This association was further confirmed in a second validation cohort comprising refractory mCRC patients treated with cetuximab ± irinotecan (PFS 8.7 vs. 3.7 months) in univariable ( P = 0.033) and multivariable analyses ( P = 0.046). Conclusions: GC rs4588 SNP might serve as a predictive marker in mCRC patients treated with FOLFIRI/bevacizumab or FOLFIRI/cetuximab. Whereas AA carriers derive a survival benefit with FOLFIRI/cetuximab, treatment with FOLFIRI/bevacizumab is associated with a worse outcome. Clin Cancer Res; 24(4); 784-93. ©2017 AACR . ©2017 American Association for Cancer Research.
Could the outcome of the 2016 US elections have been predicted from past voting patterns?

CSIR Research Space (South Africa)

Schmitz, Peter MU

2017-07-01

Full Text Available In South Africa, a team of analysts has for some years been using statistical techniques to predict election outcomes during election nights in South Africa. The prediction method involves using statistical clusters based on past voting patterns...
A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification.

Science.gov (United States)

Ponasenko, Anastasia V; Khutornaya, Maria V; Kutikhin, Anton G; Rutkovskaya, Natalia V; Tsepokina, Anna V; Kondyukova, Natalia V; Yuzhalin, Arseniy E; Barbarash, Leonid S

2016-08-31

Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification.
Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes

International Nuclear Information System (INIS)

Warnat, Patrick; Oberthuer, André; Fischer, Matthias; Westermann, Frank; Eils, Roland; Brors, Benedikt

2007-01-01

Neuroblastoma patients show heterogeneous clinical courses ranging from life-threatening progression to spontaneous regression. Recently, gene expression profiles of neuroblastoma tumours were associated with clinically different phenotypes. However, such data is still rare for important patient subgroups, such as patients with MYCN non-amplified advanced stage disease. Prediction of the individual course of disease and optimal therapy selection in this cohort is challenging. Additional research effort is needed to describe the patterns of gene expression in this cohort and to identify reliable prognostic markers for this subset of patients. We combined gene expression data from two studies in a meta-analysis in order to investigate differences in gene expression of advanced stage (3 or 4) tumours without MYCN amplification that show contrasting outcomes (alive or dead) at five years after initial diagnosis. In addition, a predictive model for outcome was generated. Gene expression profiles from 66 patients were included from two studies using different microarray platforms. In the combined data set, 72 genes were identified as differentially expressed by meta-analysis at a false discovery rate (FDR) of 8.33%. Meta-analysis detected 34 differentially expressed genes that were not found as significant in either single study. Outcome prediction based on data of both studies resulted in a predictive accuracy of 77%. Moreover, the genes that were differentially expressed in subgroups of advanced stage patients without MYCN amplification accurately separated MYCN amplified tumours from low stage tumours without MYCN amplification. Our findings support the hypothesis that neuroblastoma consists of two biologically distinct subgroups that differ by characteristic gene expression patterns, which are associated with divergent clinical outcome
Minimal gene selection for classification and diagnosis prediction based on gene expression profile

Directory of Open Access Journals (Sweden)

Alireza Mehridehnavi

2013-01-01

Conclusion: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.
A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling.

Science.gov (United States)

Klein, Eric A; Cooperberg, Matthew R; Magi-Galluzzi, Cristina; Simko, Jeffry P; Falzarano, Sara M; Maddala, Tara; Chan, June M; Li, Jianbo; Cowan, Janet E; Tsiatis, Athanasios C; Cherbavaz, Diana B; Pelham, Robert J; Tenggara-Hunter, Imelda; Baehner, Frederick L; Knezevic, Dejan; Febbo, Phillip G; Shak, Steven; Kattan, Michael W; Lee, Mark; Carroll, Peter R

2014-09-01

Prostate tumor heterogeneity and biopsy undersampling pose challenges to accurate, individualized risk assessment for men with localized disease. To identify and validate a biopsy-based gene expression signature that predicts clinical recurrence, prostate cancer (PCa) death, and adverse pathology. Gene expression was quantified by reverse transcription-polymerase chain reaction for three studies-a discovery prostatectomy study (n=441), a biopsy study (n=167), and a prospectively designed, independent clinical validation study (n=395)-testing retrospectively collected needle biopsies from contemporary (1997-2011) patients with low to intermediate clinical risk who were candidates for active surveillance (AS). The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profiles were used together with clinical and pathologic characteristics to evaluate clinical utility. Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were predictive of aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined into the GPS algorithm. In the validation study, GPS predicted high-grade (odds ratio [OR] per 20 GPS units: 2.3; 95% confidence interval [CI], 1.5-3.7; p<0.001) and high-stage (OR per 20 GPS units: 1.9; 95% CI, 1
Motivation and Treatment Credibility Predicts Dropout, Treatment Adherence, and Clinical Outcomes in an Internet-Based Cognitive Behavioral Relaxation Program: A Randomized Controlled Trial.

Science.gov (United States)

Alfonsson, Sven; Olsson, Erik; Hursti, Timo

2016-03-08

In previous research, variables such as age, education, treatment credibility, and therapeutic alliance have shown to affect patients' treatment adherence and outcome in Internet-based psychotherapy. A more detailed understanding of how such variables are associated with different measures of adherence and clinical outcomes may help in designing more effective online therapy. The aims of this study were to investigate demographical, psychological, and treatment-specific variables that could predict dropout, treatment adherence, and treatment outcomes in a study of online relaxation for mild to moderate stress symptoms. Participant dropout and attrition as well as data from self-report instruments completed before, during, and after the online relaxation program were analyzed. Multiple linear and logistical regression analyses were conducted to predict early dropout, overall attrition, online treatment progress, number of registered relaxation exercises, posttreatment symptom levels, and reliable improvement. Dropout was significantly predicted by treatment credibility, whereas overall attrition was associated with reporting a focus on immediate consequences and experiencing a low level of intrinsic motivation for the treatment. Treatment progress was predicted by education level and treatment credibility, whereas number of registered relaxation exercises was associated with experiencing intrinsic motivation for the treatment. Posttreatment stress symptoms were positively predicted by feeling external pressure to participate in the treatment and negatively predicted by treatment credibility. Reporting reliable symptom improvement after treatment was predicted by treatment credibility and therapeutic bond. This study confirmed that treatment credibility and a good working alliance are factors associated with successful Internet-based psychotherapy. Further, the study showed that measuring adherence in different ways provides somewhat different results, which
Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

Science.gov (United States)

Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

2018-04-01

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.
False positive reduction in protein-protein interaction predictions using gene ontology annotations

Directory of Open Access Journals (Sweden)

Lin Yen-Han

2007-07-01

Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially
The cure: design and evaluation of a crowdsourcing game for gene selection for breast cancer survival prediction.

Science.gov (United States)

Good, Benjamin M; Loguercio, Salvatore; Griffith, Obi L; Nanis, Max; Wu, Chunlei; Su, Andrew I

2014-07-29

Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility, and biological interpretability. Methods that take advantage of structured prior knowledge (eg, protein interaction networks) show promise in helping to define better signatures, but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes unheard of before. The main objective of this study was to test the hypothesis that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from players of an open, Web-based game. We envisioned capturing knowledge both from the player's prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. We developed and evaluated an online game called The Cure that captured information from players regarding genes for use as predictors of breast cancer survival. Information gathered from game play was aggregated using a voting approach, and used to create rankings of genes. The top genes from these rankings were evaluated using annotation enrichment analysis, comparison to prior predictor gene sets, and by using them to train and test machine learning systems for predicting 10 year survival. Between its launch in September 2012 and September 2013, The Cure attracted more than 1000 registered players, who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data showed significant enrichment for genes known to be related to key concepts such as cancer, disease progression, and recurrence. In terms of the predictive accuracy of models trained using this
Combining Spot Sign and Intracerebral Hemorrhage Score to Estimate Functional Outcome: Analysis From the PREDICT Cohort.

Science.gov (United States)

Schneider, Hauke; Huynh, Thien J; Demchuk, Andrew M; Dowlatshahi, Dar; Rodriguez-Luna, David; Silva, Yolanda; Aviv, Richard; Dzialowski, Imanuel

2018-06-01

The intracerebral hemorrhage (ICH) score is the most commonly used grading scale for stratifying functional outcome in patients with acute ICH. We sought to determine whether a combination of the ICH score and the computed tomographic angiography spot sign may improve outcome prediction in the cohort of a prospective multicenter hemorrhage trial. Prospectively collected data from 241 patients from the observational PREDICT study (Prediction of Hematoma Growth and Outcome in Patients With Intracerebral Hemorrhage Using the CT-Angiography Spot Sign) were analyzed. Functional outcome at 3 months was dichotomized using the modified Rankin Scale (0-3 versus 4-6). Performance of (1) the ICH score and (2) the spot sign ICH score-a scoring scale combining ICH score and spot sign number-was tested. Multivariable analysis demonstrated that ICH score (odds ratio, 3.2; 95% confidence interval, 2.2-4.8) and spot sign number (n=1: odds ratio, 2.7; 95% confidence interval, 1.1-7.4; n>1: odds ratio, 3.8; 95% confidence interval, 1.2-17.1) were independently predictive of functional outcome at 3 months with similar odds ratios. Prediction of functional outcome was not significantly different using the spot sign ICH score compared with the ICH score alone (spot sign ICH score area under curve versus ICH score area under curve: P =0.14). In the PREDICT cohort, a prognostic score adding the computed tomographic angiography-based spot sign to the established ICH score did not improve functional outcome prediction compared with the ICH score. © 2018 American Heart Association, Inc.
Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

Science.gov (United States)

Alshurafa, Nabil; Sideris, Costas; Pourhomayoun, Mohammad; Kalantarian, Haik; Sarrafzadeh, Majid; Eastwood, Jo-Ann

2017-03-01

Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support. Many participants benefitted from this RHM system. In response to the variance in participants' success, we developed a framework to identify classification schemes that predicted successful and unsuccessful participants. We analyzed both contextual baseline features and data from the first month of intervention such as activity, blood pressure, and questionnaire responses transmitted through the smartphone. A prediction tool can aid clinicians and scientists in identifying participants who may optimally benefit from the RHM system. Targeting therapies could potentially save healthcare costs, clinician, and participant time and resources. Our classification scheme yields RHM outcome success predictions with an F-measure of 91.9%, and identifies behaviors during the first month of intervention that help determine outcome success. We also show an improvement in prediction by using intervention-based smartphone data. Results from the WHHS study demonstrates that factors such as the variation in first month intervention response to the consumption of nuts, beans, and seeds in the diet help predict patient RHM protocol outcome success in a group of young Black women ages 25-45.
Gene Expression Differences Predict Treatment Outcome of Merkel Cell Carcinoma Patients

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Loren Masterson

2014-01-01

Full Text Available Due to the rarity of Merkel cell carcinoma (MCC, prospective clinical trials have not been practical. This study aimed to identify biomarkers with prognostic significance. While sixty-two patients were identified who were treated for MCC at our institution, only seventeen patients had adequate formalin-fixed paraffin-embedded archival tissue and followup to be included in the study. Patients were stratified into good, moderate, or poor prognosis. Laser capture microdissection was used to isolate tumor cells for subsequent RNA isolation and gene expression analysis with Affymetrix GeneChip Human Exon 1.0 ST arrays. Among the 191 genes demonstrating significant differential expression between prognostic groups, keratin 20 and neurofilament protein have previously been identified in studies of MCC and were significantly upregulated in tumors from patients with a poor prognosis. Immunohistochemistry further established that keratin 20 was overexpressed in the poor prognosis tumors. In addition, novel genes of interest such as phospholipase A2 group X, kinesin family member 3A, tumor protein D52, mucin 1, and KIT were upregulated in specimens from patients with poor prognosis. Our pilot study identified several gene expression differences which could be used in the future as prognostic biomarkers in MCC patients.
Identification and Construction of Combinatory Cancer Hallmark-Based Gene Signature Sets to Predict Recurrence and Chemotherapy Benefit in Stage II Colorectal Cancer.

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Gao, Shanwu; Tibiche, Chabane; Zou, Jinfeng; Zaman, Naif; Trifiro, Mark; O'Connor-McCourt, Maureen; Wang, Edwin

2016-01-01

Decisions regarding adjuvant therapy in patients with stage II colorectal cancer (CRC) have been among the most challenging and controversial in oncology over the past 20 years. To develop robust combinatory cancer hallmark-based gene signature sets (CSS sets) that more accurately predict prognosis and identify a subset of patients with stage II CRC who could gain survival benefits from adjuvant chemotherapy. Thirteen retrospective studies of patients with stage II CRC who had clinical follow-up and adjuvant chemotherapy were analyzed. Respective totals of 162 and 843 patients from 2 and 11 independent cohorts were used as the discovery and validation cohorts, respectively. A total of 1005 patients with stage II CRC were included in the 13 cohorts. Among them, 84 of 416 patients in 3 independent cohorts received fluorouracil-based adjuvant chemotherapy. Identification of CSS sets to predict relapse-free survival and identify a subset of patients with stage II CRC who could gain substantial survival benefits from fluorouracil-based adjuvant chemotherapy. Eight cancer hallmark-based gene signatures (30 genes each) were identified and used to construct CSS sets for determining prognosis. The CSS sets were validated in 11 independent cohorts of 767 patients with stage II CRC who did not receive adjuvant chemotherapy. The CSS sets accurately stratified patients into low-, intermediate-, and high-risk groups. Five-year relapse-free survival rates were 94%, 78%, and 45%, respectively, representing 60%, 28%, and 12% of patients with stage II disease. The 416 patients with CSS set-defined high-risk stage II CRC who received fluorouracil-based adjuvant chemotherapy showed a substantial gain in survival benefits from the treatment (ie, recurrence reduced by 30%-40% in 5 years). The CSS sets substantially outperformed other prognostic predictors of stage 2 CRC. They are more accurate and robust for prognostic predictions and facilitate the identification of patients with stage
Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .
A comparison of machine learning techniques for survival prediction in breast cancer.

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Vanneschi, Leonardo; Farinaccio, Antonella; Mauri, Giancarlo; Antoniotti, Mauro; Provero, Paolo; Giacobini, Mario

2011-05-11

The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.
A comparison of machine learning techniques for survival prediction in breast cancer

Directory of Open Access Journals (Sweden)

Vanneschi Leonardo

2011-05-01

Full Text Available Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.
Hepatitis B virus DNA polymerase gene polymorphism based ...

African Journals Online (AJOL)

Hepatitis B virus DNA polymerase gene polymorphism based prediction of genotypes in chronic HBV patients from Western India. Yashwant G. Chavan, Sharad R. Pawar, Minal Wani, Amol D. Raut, Rabindra N. Misra ...

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

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He, Zhili; Zhang, Ping; Wu, Linwei; Rocha, Andrea M; Tu, Qichao; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D; Wu, Liyou; Yang, Yunfeng; Elias, Dwayne A; Watson, David B; Adams, Michael W W; Fields, Matthew W; Alm, Eric J; Hazen, Terry C; Adams, Paul D; Arkin, Adam P; Zhou, Jizhong

2018-02-20

Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly ( P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as
A 65‑gene signature for prognostic prediction in colon adenocarcinoma.

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Jiang, Hui; Du, Jun; Gu, Jiming; Jin, Liugen; Pu, Yong; Fei, Bojian

2018-04-01

The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival‑related genes were selected from the DEGs using the Cox regression method. A co‑expression network of survival‑related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan‑Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival‑related genes selected. The co‑expression network of survival‑related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator‑activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine‑cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65‑gene signature was established using this co‑expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e‑12) and the GSE17538 dataset (P=1.67e‑6). The 65‑gene signature included kallikrein‑related peptidase 6 (KLK6), collagen type XI α1 (COL11A1), cartilage
Early Seizure Frequency and Aetiology Predict Long-Term Medical Outcome in Childhood-Onset Epilepsy

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Sillanpaa, Matti; Schmidt, Dieter

2009-01-01

In clinical practice, it is important to predict as soon as possible after diagnosis and starting treatment, which children are destined to develop medically intractable seizures and be at risk of increased mortality. In this study, we determined factors predictive of long-term seizure and mortality outcome in a population-based cohort of 102…
Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

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Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

2018-01-01

Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.
Predicting sports betting outcomes

OpenAIRE

Flis, Borut

2014-01-01

We wish to build a model, which could predict the outcome of basketball games. The goal was to achieve an sufficient enough accuracy to make a profit in sports betting. One learning example is a game in the NBA regular season. Every example has multiple features, which describe the opposing teams. We tried many methods, which return the probability of the home team winning and the probability of the away team winning. These probabilities are used for risk analysis. We used the best model in h...
Method and timing of tumor volume measurement for outcome prediction in cervical cancer using magnetic resonance imaging

International Nuclear Information System (INIS)

Mayr, Nina A.; Taoka, Toshiaki; Yuh, William T.C.; Denning, Leah M.; Zhen, Weining K.; Paulino, Arnold C.; Gaston, Robert C.; Sorosky, Joel I.; Meeks, Sanford L.; Walker, Joan L.; Mannel, Robert S.; Buatti, John M.

2002-01-01

Purpose: Recently, imaging-based tumor volume before, during, and after radiation therapy (RT) has been shown to predict tumor response in cervical cancer. However, the effectiveness of different methods and timing of imaging-based tumor size assessment have not been investigated. The purpose of this study was to compare the predictive value for treatment outcome derived from simple diameter-based ellipsoid tumor volume measurement using orthogonal diameters (with ellipsoid computation) with that derived from more complex contour tracing/region-of-interest (ROI) analysis 3D tumor volumetry. Methods and Materials: Serial magnetic resonance imaging (MRI) examinations were prospectively performed in 60 patients with advanced cervical cancer (Stages IB 2 -IVB/recurrent) at the start of RT, during early RT (20-25 Gy), mid-RT (45-50 Gy), and at follow-up (1-2 months after RT completion). ROI-based volumetry was derived by tracing the entire tumor region in each MR slice on the computer work station. For the diameter-based surrogate ''ellipsoid volume,'' the three orthogonal diameters (d 1 , d 2 , d 3 ) were measured on film hard copies to calculate volume as an ellipsoid (d 1 x d 2 x d 3 x π/6). Serial tumor volumes and regression rates determined by each method were correlated with local control, disease-free and overall survival, and the results were compared between the two measuring methods. Median post-therapy follow-up was 4.9 years (range, 2.0-8.2 years). Results: The best method and time point of tumor size measurement for the prediction of outcome was the tumor regression rate in the mid-therapy MRI examination (at 45-50 Gy) using 3D ROI volumetry. For the pre-RT measurement both the diameter-based method and ROI volumetry provided similar predictive accuracy, particularly for patients with small ( 3 ) and large (≥100 cm 3 ) pre-RT tumor size. However, the pre-RT tumor size measured by either method had much less predictive value for the intermediate-size (40
Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression.

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Guilloux, Jean-Philippe; Bassi, Sabrina; Ding, Ying; Walsh, Chris; Turecki, Gustavo; Tseng, George; Cyranowski, Jill M; Sibille, Etienne

2015-02-01

Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).
Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC Study by Biochemically-inspired Machine Learning [version 3; referees: 2 approved

Directory of Open Access Journals (Sweden)

Eliseos J. Mucaki

2017-05-01

Full Text Available Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT and, in some cases, chemotherapy (CT agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM model of paclitaxel response containing genes ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr. Accuracy was lower (73.4% in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes BCL2L1, BBC3, FGF2, FN1, and TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF classifier using a gene signature (ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for
Outcome manipulation in corporate prediction markets

DEFF Research Database (Denmark)

Ottaviani, Marco; Sørensen, Peter Norman

2007-01-01

This paper presents a framework for applying prediction markets to corporate decision-making. The analysis is motivated by the recent surge of interest in markets as information aggregation devices and their potential use within firms. We characterize the amount of outcome manipulation that results...
A new nomogram to predict pathologic outcome following radical prostatectomy

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Alexandre Crippa

2006-04-01

Full Text Available OBJECTIVE: To develop a preoperative nomogram to predict pathologic outcome in patients submitted to radical prostatectomy for clinical localized prostate cancer. MATERIALS AND METHODS: Nine hundred and sixty patients with clinical stage T1 and T2 prostate cancer were evaluated following radical prostatectomy, and 898 were included in the study. Following a multivariate analysis, nomograms were developed incorporating serum PSA, biopsy Gleason score, and percentage of positive biopsy cores in order to predict the risks of extraprostatic tumor extension, and seminal vesicle involvement. RESULTS: In univariate analysis there was a significant association between percentage of positive biopsy cores (p < 0.001, serum PSA (p = 0.001 and biopsy Gleason score (p < 0.001 with extraprostatic tumor extension. A similar pathologic outcome was seen among tumors with Gleason score 7, and Gleason score 8 to 10. In multivariate analysis, the 3 preoperative variables showed independent significance to predict tumor extension. This allowed the development of nomogram-1 (using Gleason scores in 3 categories - 2 to 6, 7 and 8 to 10 and nomogram-2 (using Gleason scores in 2 categories - 2 to 6 and 7 to 10 to predict disease extension based on these 3 parameters. In the validation analysis, 87% and 91.1% of the time the nomograms-1 and 2, correctly predicted the probability of a pathological stage to within 10% respectively. CONCLUSION: Incorporating percent of positive biopsy cores to a nomogram that includes preoperative serum PSA and biopsy Gleason score, can accurately predict the presence of extraprostatic disease extension in patients with clinical localized prostate cancer.
Multi-label literature classification based on the Gene Ontology graph

Directory of Open Access Journals (Sweden)

Lu Xinghua

2008-12-01

Full Text Available Abstract Background The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. Results In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Conclusion Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate
Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

Science.gov (United States)

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.
Expression Pattern Similarities Support the Prediction of Orthologs Retaining Common Functions after Gene Duplication Events1[OPEN

Science.gov (United States)

Haberer, Georg; Panda, Arup; Das Laha, Shayani; Ghosh, Tapas Chandra; Schäffner, Anton R.

2016-01-01

The identification of functionally equivalent, orthologous genes (functional orthologs) across genomes is necessary for accurate transfer of experimental knowledge from well-characterized organisms to others. This frequently relies on automated, coding sequence-based approaches such as OrthoMCL, Inparanoid, and KOG, which usually work well for one-to-one homologous states. However, this strategy does not reliably work for plants due to the occurrence of extensive gene/genome duplication. Frequently, for one query gene, multiple orthologous genes are predicted in the other genome, and it is not clear a priori from sequence comparison and similarity which one preserves the ancestral function. We have studied 11 organ-dependent and stress-induced gene expression patterns of 286 Arabidopsis lyrata duplicated gene groups and compared them with the respective Arabidopsis (Arabidopsis thaliana) genes to predict putative expressologs and nonexpressologs based on gene expression similarity. Promoter sequence divergence as an additional tool to substantiate functional orthology only partially overlapped with expressolog classification. By cloning eight A. lyrata homologs and complementing them in the respective four Arabidopsis loss-of-function mutants, we experimentally proved that predicted expressologs are indeed functional orthologs, while nonexpressologs or nonfunctionalized orthologs are not. Our study demonstrates that even a small set of gene expression data in addition to sequence homologies are instrumental in the assignment of functional orthologs in the presence of multiple orthologs. PMID:27303025
A commercial outcome prediction system for university technology transfer using neural networks

OpenAIRE

Chu, Ling

2007-01-01

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 26/03/2007. This thesis presents a commercial outcome prediction system (CPS) capable of predicting the likely future monetary return that would be generated by an invention. The CPS is designed to be used by university technology transfer offices for invention assessment purposes, and is based on the data from their historical invention cases. It is aimed at improving technology transfer off...
MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status.

Science.gov (United States)

Bady, Pierre; Sciuscio, Davide; Diserens, Annie-Claire; Bloch, Jocelyne; van den Bent, Martin J; Marosi, Christine; Dietrich, Pierre-Yves; Weller, Michael; Mariani, Luigi; Heppner, Frank L; Mcdonald, David R; Lacombe, Denis; Stupp, Roger; Delorenzi, Mauro; Hegi, Monika E

2012-10-01

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg12434587 [corrected] and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

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Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

2015-01-01

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516
Network-based prediction and knowledge mining of disease genes.

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Carson, Matthew B; Lu, Hui

2015-01-01

In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network
Prediction of processing tomato peeling outcomes

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Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After ...
Establishment of a 12-gene expression signature to predict colon cancer prognosis

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Dalong Sun

2018-06-01

Full Text Available A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA. The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS: Kaplan Meier (KM Log Rank p = 0.0034; overall survival (OS: KM Log Rank p = 0.0336 in GSE17538. For patients with proficient mismatch repair system (pMMR in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS: KM Log Rank p = 0.022. Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher’s exact test p = 0.0003. After stage stratification, the signature could still distinguish poor prognosis patients in GSE17538 from good prognosis within stage II (Log Rank p = 0.01 and stage II & III (Log Rank p = 0.017 in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rank p = 0.046; III & II, OS: KM Log Rank p = 0.041. Among stage II/III pMMR patients
Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

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Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

2016-01-01

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

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Mitchell Pesesky

2016-11-01

Full Text Available The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitate initial use of empiric (frequently broad-spectrum antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0% and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance
The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

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Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.
Predicting outcome of acute kidney transplant rejection using

NARCIS (Netherlands)

Rekers, Niels Vincent

2014-01-01

Acute kidney transplant rejection is an important risk factors for adverse graft outcome. Once diagnosed, it remains difficult to predict the risk of graft loss and the response to anti-rejection treatment. The aim of this thesis was to identify biomarkers during acute rejection, which predict the
Predicting successful treatment outcome of web-based self-help for problem drinkers: secondary analysis from a randomized controlled trial.

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Riper, Heleen; Kramer, Jeannet; Keuken, Max; Smit, Filip; Schippers, Gerard; Cuijpers, Pim

2008-11-22

Web-based self-help interventions for problem drinking are coming of age. They have shown promising results in terms of cost-effectiveness, and they offer opportunities to reach out on a broad scale to problem drinkers. The question now is whether certain groups of problem drinkers benefit more from such Web-based interventions than others. We sought to identify baseline, client-related predictors of the effectiveness of Drinking Less, a 24/7, free-access, interactive, Web-based self-help intervention without therapist guidance for problem drinkers who want to reduce their alcohol consumption. The intervention is based on cognitive-behavioral and self-control principles. We conducted secondary analysis of data from a pragmatic randomized trial with follow-up at 6 and 12 months. Participants (N = 261) were adult problem drinkers in the Dutch general population with a weekly alcohol consumption above 210 g of ethanol for men or 140 g for women, or consumption of at least 60 g (men) or 40 g (women) one or more days a week over the past 3 months. Six baseline participant characteristics were designated as putative predictors of treatment response: (1) gender, (2) education, (3) Internet use competence (sociodemographics), (4) mean weekly alcohol consumption, (5) prior professional help for alcohol problems (level of problem drinking), and (6) participants' expectancies of Web-based interventions for problem drinking. Intention-to-treat (ITT) analyses, using last-observation-carried-forward (LOCF) data, and regression imputation (RI) were performed to deal with loss to follow-up. Statistical tests for interaction terms were conducted and linear regression analysis was performed to investigate whether the participants' characteristics as measured at baseline predicted positive treatment responses at 6- and 12-month follow-ups. At 6 months, prior help for alcohol problems predicted a small, marginally significant positive treatment outcome in the RI model only (beta = .18
Wilms’ Tumor 1 Gene Mutations Independently Predict Poor Outcome in Adults With Cytogenetically Normal Acute Myeloid Leukemia: A Cancer and Leukemia Group B Study

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Paschka, Peter; Marcucci, Guido; Ruppert, Amy S.; Whitman, Susan P.; Mrózek, Krzysztof; Maharry, Kati; Langer, Christian; Baldus, Claudia D.; Zhao, Weiqiang; Powell, Bayard L.; Baer, Maria R.; Carroll, Andrew J.; Caligiuri, Michael A.; Kolitz, Jonathan E.; Larson, Richard A.; Bloomfield, Clara D.

2008-01-01

Purpose To analyze the prognostic impact of Wilms’ tumor 1 (WT1) gene mutations in cytogenetically normal acute myeloid leukemia (CN-AML). Patients and Methods We studied 196 adults younger than 60 years with newly diagnosed primary CN-AML, who were treated similarly on Cancer and Leukemia Group B (CALGB) protocols 9621 and 19808, for WT1 mutations in exons 7 and 9. The patients also were assessed for the presence of FLT3 internal tandem duplications (FLT3-ITD), FLT3 tyrosine kinase domain mutations (FLT3-TKD), MLL partial tandem duplications (MLL-PTD), NPM1 and CEBPA mutations, and for the expression levels of ERG and BAALC. Results Twenty-one patients (10.7%) harbored WT1 mutations. Complete remission rates were not significantly different between patients with WT1 mutations and those with unmutated WT1 (P = .36; 76% v 84%). Patients with WT1 mutations had worse disease-free survival (DFS; P < .001; 3-year rates, 13% v 50%) and overall survival (OS; P < .001; 3-year rates, 10% v 56%) than patients with unmutated WT1. In multivariable analyses, WT1 mutations independently predicted worse DFS (P = .009; hazard ratio [HR] = 2.7) when controlling for CEBPA mutational status, ERG expression level, and FLT3-ITD/NPM1 molecular-risk group (ie, FLT3-ITDnegative/NPM1mutated as low risk v FLT3-ITDpositive and/or NPM1wild-type as high risk). WT1 mutations also independently predicted worse OS (P < .001; HR = 3.2) when controlling for CEBPA mutational status, FLT3-ITD/NPM1 molecular-risk group, and white blood cell count. Conclusion We report the first evidence that WT1 mutations independently predict extremely poor outcome in intensively treated, younger patients with CN-AML. Future trials should include testing for WT1 mutations as part of molecularly based risk assessment and risk-adapted treatment stratification of patients with CN-AML. PMID:18559874
[Encopresis--predictive factors and outcome].

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Mehler-Wex, Claudia; Scheuerpflug, Peter; Peschke, Nicole; Roth, Michael; Reitzle, Karl; Warnke, Andreas

2005-10-01

comparison of diagnostic, clinical and therapeutic features and their predictive value for the outcome of encopresis in children and adolescents. 85 children and adolescents (aged 9.6 +/- 3.2 years) with severe encopresis (ICD 10: F98.1) were investigated during inpatient treatment and 35 of them again 5.5 +/- 1.8 years later. Mentally retarded patients were excluded. Inpatient therapy consisted of treating constipation and/or stool regulation by means of laxatives, behavioural approaches, and the specific therapy of comorbid psychiatric disorders. During inpatient treatment 22% of the patients experienced total remission, 8% an unchanged persistence of symptoms. Of the 35 patients studied at follow-up 5.5 years later, 40% were symptom-free. As main result, prognostic outcome depended significantly on sufficient treatment of obstipation. Another important factor was the specific therapeutic approach to psychiatric comorbidity, especially to ADHD. The outcome for patients with comorbid ICD 10: F43 was significantly better than for the other patients. Those who were symptom-free at discharge had significantly better long-term outcomes. Decisive to the success of encopresis treatment were the stool regulation and the specific therapy of associated psychiatric illnesses, in particular of ADHD. Inpatient treatment revealed significantly better long-term outcomes where total remission had been achieved by the time of discharge from hospital.
Associations between HIV-RNA-based indicators and virological and clinical outcomes

DEFF Research Database (Denmark)

Laut, Kamilla G; Shepherd, Leah C; Pedersen, Court

2016-01-01

OBJECTIVES: To evaluate and compare the performance of six HIV-RNA-based quality of care indicators for predicting short-term and long-term outcomes. DESIGN: Multinational cohort study. METHODS: We included EuroSIDA patients on antiretroviral therapy (ART) with at least three viral load measureme......OBJECTIVES: To evaluate and compare the performance of six HIV-RNA-based quality of care indicators for predicting short-term and long-term outcomes. DESIGN: Multinational cohort study. METHODS: We included EuroSIDA patients on antiretroviral therapy (ART) with at least three viral load...... measurements after baseline (the latest of 01/01/2001 or entry into EuroSIDA). Using multivariate Poisson regression, we modelled the association between short-term (resistance, triple-class failure) and long-term (all-cause mortality, any AIDS/non-AIDS clinical event) outcomes and the indicators: viraemia...
Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

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Liying Yang

2016-01-01

Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.
Predicting outcome in term neonates with hypoxic-ischaemic encephalopathy using simplified MR criteria

International Nuclear Information System (INIS)

Jyoti, Rajeev; O'Neil, Ross

2006-01-01

MRI is an established investigation in the evaluation of neonates with suspected hypoxic-ischaemic encephalopathy (HIE). However, its role as a predictor of neurodevelopmental outcome remains complex. To establish reproducible simplified MR criteria and evaluate their role in predicting neurodevelopmental outcome in term neonates with HIE. Term neonates with suspected HIE had MRI at 7-10 days of age. MR scans were interpreted according to new simplified criteria by two radiologists blinded to the clinical course and outcome. The new simplified criteria allocated grade 1 to cases with no central and less than 10% peripheral change, grade 2 to those with less than 30% central and/or 10-30% peripheral area change, and grade 3 to those with more than 30% central or peripheral change. MRI changes were compared with clinical neurodevelopmental outcome evaluated prospectively at 1 year of age. Neurodevelopmental outcome was based upon the DQ score (revised Griffith's) and cerebral palsy on neurological assessment. Of 20 subjects, all those showing severe (grade 3) MR changes (35%) died or had poor neurodevelopmental outcome. Subjects with a normal MR scan or with scans showing only mild (grade 1) MR changes (55%) had normal outcomes. One subject showing moderate (grade 2) changes on MRI had a moderate outcome (5%), while another had an atypical pattern of MR changes with a normal outcome (5%). Assessment of full-term neonates with suspected HIE using the simplified MR criteria is highly predictive of neurodevelopmental outcome. (orig.)
Predicting outcome in term neonates with hypoxic-ischaemic encephalopathy using simplified MR criteria

Energy Technology Data Exchange (ETDEWEB)

Jyoti, Rajeev; O' Neil, Ross [Canberra Hospital, Medical Imaging, Canberra, ACT (Australia)

2006-01-01

MRI is an established investigation in the evaluation of neonates with suspected hypoxic-ischaemic encephalopathy (HIE). However, its role as a predictor of neurodevelopmental outcome remains complex. To establish reproducible simplified MR criteria and evaluate their role in predicting neurodevelopmental outcome in term neonates with HIE. Term neonates with suspected HIE had MRI at 7-10 days of age. MR scans were interpreted according to new simplified criteria by two radiologists blinded to the clinical course and outcome. The new simplified criteria allocated grade 1 to cases with no central and less than 10% peripheral change, grade 2 to those with less than 30% central and/or 10-30% peripheral area change, and grade 3 to those with more than 30% central or peripheral change. MRI changes were compared with clinical neurodevelopmental outcome evaluated prospectively at 1 year of age. Neurodevelopmental outcome was based upon the DQ score (revised Griffith's) and cerebral palsy on neurological assessment. Of 20 subjects, all those showing severe (grade 3) MR changes (35%) died or had poor neurodevelopmental outcome. Subjects with a normal MR scan or with scans showing only mild (grade 1) MR changes (55%) had normal outcomes. One subject showing moderate (grade 2) changes on MRI had a moderate outcome (5%), while another had an atypical pattern of MR changes with a normal outcome (5%). Assessment of full-term neonates with suspected HIE using the simplified MR criteria is highly predictive of neurodevelopmental outcome. (orig.)
Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels

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Chika Sumiyoshi

2015-09-01

Full Text Available Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1 to identify which outcome factors predict occupational functioning, quantified as work hours, and 2 to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB, the UCSD Performance-based Skills Assessment-Brief (UPSA-B, and the Social Functioning Scale Individuals’ version modified for the MATRICS-PASS (Modified SFS for PASS, respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly and a multiple logistic regression analyses (predicting dichotomized work status based on work hours. ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60–70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.
Hopefulness predicts resilience after hereditary colorectal cancer genetic testing: a prospective outcome trajectories study.

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Ho, Samuel M Y; Ho, Judy W C; Bonanno, George A; Chu, Annie T W; Chan, Emily M S

2010-06-11

Genetic testing for hereditary colorectal cancer (HCRC) had significant psychological consequences for test recipients. This prospective longitudinal study investigated the factors that predict psychological resilience in adults undergoing genetic testing for HCRC. A longitudinal study was carried out from April 2003 to August 2006 on Hong Kong Chinese HCRC family members who were recruited and offered genetic testing by the Hereditary Gastrointestinal Cancer Registry to determine psychological outcomes after genetic testing. Self-completed questionnaires were administered immediately before (pre-disclosure baseline) and 2 weeks, 4 months and 1 year after result disclosure. Using validated psychological inventories, the cognitive style of hope was measured at baseline, and the psychological distress of depression and anxiety was measured at all time points. Of the 76 participating subjects, 71 individuals (43 men and 28 women; mean age 38.9 +/- 9.2 years) from nine FAP and 24 HNPCC families completed the study, including 39 mutated gene carriers. Four patterns of outcome trajectories were created using established norms for the specified outcome measures of depression and anxiety. These included chronic dysfunction (13% and 8.7%), recovery (0% and 4.3%), delayed dysfunction (13% and 15.9%) and resilience (76.8% and 66.7%). Two logistic regression analyses were conducted using hope at baseline to predict resilience, with depression and anxiety employed as outcome indicators. Because of the small number of participants, the chronic dysfunction and delayed dysfunction groups were combined into a non-resilient group for comparison with the resilient group in all subsequent analysis. Because of low frequencies, participants exhibiting a recovery trajectory (n = 3 for anxiety and n = 0 for depression) were excluded from further analysis. Both regression equations were significant. Baseline hope was a significant predictor of a resilience outcome trajectory for depression
Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

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Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.
Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower

Directory of Open Access Journals (Sweden)

Patrick Thorwarth

2018-02-01

Full Text Available Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding.
Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression.

Science.gov (United States)

Hollander, Zsuzsanna; Chen, Virginia; Sidhu, Keerat; Lin, David; Ng, Raymond T; Balshaw, Robert; Cohen-Freue, Gabriela V; Ignaszewski, Andrew; Imai, Carol; Kaan, Annemarie; Tebbutt, Scott J; Wilson-McManus, Janet E; McMaster, Robert W; Keown, Paul A; McManus, Bruce M

2013-02-01

Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment. Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques. The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK. Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring. Copyright © 2013 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.
The use of presurgical psychological screening to predict the outcome of spine surgery.

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Block, A R; Ohnmeiss, D D; Guyer, R D; Rashbaum, R F; Hochschuler, S H

2001-01-01

Several previous studies have shown that psychosocial factors can influence the outcome of elective spine surgery. The purpose of the current study was to determine how well a presurgical screening instrument could predict surgical outcome. The study was conducted by staff of a psychologist's office. They performed preoperative screening for spine surgery candidates and collected the follow-up data. Presurgical screening and follow-up data collection was performed on 204 patients who underwent laminectomy/discectomy (n=118) or fusion (n=86) of the lumbar spine. The outcome measures used in the study were visual analog pain scales, the Oswestry Disability Questionnaire, and medication use. A semi-structured interview and psychometric testing were used to identify specific, quantifiable psychological, and "medical" risk factors for poor surgical outcome. A presurgical psychological screening (PPS) scorecard was completed for each patient, assessing whether the patient had a high or low level of risk on these psychological and medical dimensions. Based on the scorecard, an overall surgical prognosis of "good," "fair," or "poor" was generated. Results showed spine surgery led to significant overall improvements in pain, functional ability, and medication use. Medical and psychological risk levels were significantly related to outcome, with the poorest results obtained by patients having both high psychological and medical risk. Further, the accuracy of PPS surgical prognosis in predicting overall outcome was 82%. Only 9 of 53 patients predicted to have poor outcome achieved fair or good results from spine surgery. These findings suggest that PPS should become a more routine part of the evaluation of chronic pain patients in whom spine surgery is being considered.
Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

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Jing Lu

2014-11-01

Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
[Predictive factors of the outcomes of prenatal hydronephrosis.

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Bragagnini, Paolo; Estors, Blanca; Delgado, Reyes; Rihuete, Miguel Ángel; Gracia, Jesús

2016-12-01

To determine prenatal and postnatal independent predictors of poor outcome, spontaneous resolution, or the need for surgery in patients with prenatal hydronephrosis. We performed a retrospective study of patients with prenatal hydronephrosis. The renal pelvis APD was measured in the third prenatal trimester ultrasound, as well as in the first and second postnatal ultrasound. Other variables were taken into account, both prenatal and postnatal. For statistical analysis we used Student t-test, chi-square test, survival analysis, logrank test, and ROC curves. We included 218 patients with 293 renal units (RU). Of these, 147/293 (50.2%) RU were operated. 76/293 (25.9%) RU had spontaneous resolution and other 76/293 (25.9%) RU had poor outcome. As risk factors for surgery we found low birth weight (OR 3.84; 95% CI 1.24-11.84), prematurity (OR 4.17; 95% CI 1.35-12.88), duplication (OR 4.99; 95% CI 2.21-11.23) and the presence of nephrourological underlying pathology (OR 53.54; 95% CI 26.23-109.27). For the non-spontaneous resolution, we found as risk factors the alterations of amniotic fluid volume (RR 1.46; 95% CI 1.33-1.60) as well as the underlying nephrourological pathology and duplication. In the poor outcome, we found as risk factors the alterations of amniotic fluid volume (OR 4.54; 95% CI 1.31-15.62), the presence of nephrourological pathology (OR 4.81 95% CI 2.60-8.89) and RU that was operated (OR 4.23, 95% CI 2.35-7.60). The APD of the renal pelvis in all three ultrasounds were reliable for surgery prediction (area under the curve 0.65; 0.82; 0.71) or spontaneous resolution (area under the curve 0.80; 0.91; 0.80), only the first postnatal ultrasound has predictive value in the poor outcome (area under the curve 0.73). The higher sensitivity and specificity of the APD as predictor value was on the first postnatal ultrasound, 14.60 mm for surgery; 11.35 mm for spontaneous resolution and 15.50 mm for poor outcome. The higher APD in the renal pelvis in any of the
Gene Polymorphism-related Individual and Interracial Differences in the Outcomes of Androgen Deprivation Therapy for Prostate Cancer.

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Fujimoto, Naohiro; Shiota, Masaki; Tomisaki, Ikko; Minato, Akinori

2017-06-01

Among patients with prostate cancer, the prognosis after androgen deprivation therapy differs significantly among individuals and among races; however, the reasons underlying these differences are poorly understood. Several single nucleotide polymorphisms in genes associated with prostate cancer progression or castration resistance might serve as the host factor that influences prognosis and, thus, accounts for these individual and racial gaps in treatment outcomes. Accordingly, single nucleotide polymorphisms associated with treatment outcomes could be used as predictive and/or prognostic biomarkers for patient stratification and to identify personalized treatment and follow-up protocols. The present review has summarized the genetic polymorphisms that have been reported to associate with androgen deprivation therapy outcomes among patients with prostate cancer and compared the allele frequencies among different ethnic groups. Copyright © 2017 Elsevier Inc. All rights reserved.
How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology

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Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumu...

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

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Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

2012-01-01

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.
Using predictive analytics and big data to optimize pharmaceutical outcomes.

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Hernandez, Inmaculada; Zhang, Yuting

2017-09-15

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.
Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.

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Hyun-Seob Song

Full Text Available Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.
Prognostic and predictive value of VHL gene alteration in renal cell carcinoma: a meta-analysis and review.

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Kim, Bum Jun; Kim, Jung Han; Kim, Hyeong Su; Zang, Dae Young

2017-02-21

The von Hippel-Lindau (VHL) gene is often inactivated in sporadic renal cell carcinoma (RCC) by mutation or promoter hypermethylation. The prognostic or predictive value of VHL gene alteration is not well established. We conducted this meta-analysis to evaluate the association between the VHL alteration and clinical outcomes in patients with RCC. We searched PUBMED, MEDLINE and EMBASE for articles including following terms in their titles, abstracts, or keywords: 'kidney or renal', 'carcinoma or cancer or neoplasm or malignancy', 'von Hippel-Lindau or VHL', 'alteration or mutation or methylation', and 'prognostic or predictive'. There were six studies fulfilling inclusion criteria and a total of 633 patients with clear cell RCC were included in the study: 244 patients who received anti-vascular endothelial growth factor (VEGF) therapy in the predictive value analysis and 419 in the prognostic value analysis. Out of 663 patients, 410 (61.8%) had VHL alteration. The meta-analysis showed no association between the VHL gene alteration and overall response rate (relative risk = 1.47 [95% CI, 0.81-2.67], P = 0.20) or progression free survival (hazard ratio = 1.02 [95% CI, 0.72-1.44], P = 0.91) in patients with RCC who received VEGF-targeted therapy. There was also no correlation between the VHL alteration and overall survival (HR = 0.80 [95% CI, 0.56-1.14], P = 0.21). In conclusion, this meta-analysis indicates that VHL gene alteration has no prognostic or predictive value in patients with clear cell RCC.
Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study.

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Rossetti, Andrea O; Tovar Quiroga, Diego F; Juan, Elsa; Novy, Jan; White, Roger D; Ben-Hamouda, Nawfel; Britton, Jeffrey W; Oddo, Mauro; Rabinstein, Alejandro A

2017-07-01

The prognostic role of electroencephalography during and after targeted temperature management in postcardiac arrest patients, relatively to other predictors, is incompletely known. We assessed performances of electroencephalography during and after targeted temperature management toward good and poor outcomes, along with other recognized predictors. Cohort study (April 2009 to March 2016). Two academic hospitals (Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; Mayo Clinic, Rochester, MN). Consecutive comatose adults admitted after cardiac arrest, identified through prospective registries. All patients were managed with targeted temperature management, receiving prespecified standardized clinical, neurophysiologic (particularly, electroencephalography during and after targeted temperature management), and biochemical evaluations. We assessed electroencephalography variables (reactivity, continuity, epileptiform features, and prespecified "benign" or "highly malignant" patterns based on the American Clinical Neurophysiology Society nomenclature) and other clinical, neurophysiologic (somatosensory-evoked potential), and biochemical prognosticators. Good outcome (Cerebral Performance Categories 1 and 2) and mortality predictions at 3 months were calculated. Among 357 patients, early electroencephalography reactivity and continuity and flexor or better motor reaction had greater than 70% positive predictive value for good outcome; reactivity (80.4%; 95% CI, 75.9-84.4%) and motor response (80.1%; 95% CI, 75.6-84.1%) had highest accuracy. Early benign electroencephalography heralded good outcome in 86.2% (95% CI, 79.8-91.1%). False positive rates for mortality were less than 5% for epileptiform or nonreactive early electroencephalography, nonreactive late electroencephalography, absent somatosensory-evoked potential, absent pupillary or corneal reflexes, presence of myoclonus, and neuron-specific enolase greater than 75 µg/L; accuracy was highest for
Development and validation of a dynamic outcome prediction model for paracetamol-induced acute liver failure

DEFF Research Database (Denmark)

Bernal, William; Wang, Yanzhong; Maggs, James

2016-01-01

: The models developed here show very good discrimination and calibration, confirmed in independent datasets, and suggest that many patients undergoing transplantation based on existing criteria might have survived with medical management alone. The role and indications for emergency liver transplantation......BACKGROUND: Early, accurate prediction of survival is central to management of patients with paracetamol-induced acute liver failure to identify those needing emergency liver transplantation. Current prognostic tools are confounded by recent improvements in outcome independent of emergency liver...... transplantation, and constrained by static binary outcome prediction. We aimed to develop a simple prognostic tool to reflect current outcomes and generate a dynamic updated estimation of risk of death. METHODS: Patients with paracetamol-induced acute liver failure managed at intensive care units in the UK...
Predicting Positive Education Outcomes for Emerging Adults in Mental Health Systems of Care.

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Brennan, Eileen M; Nygren, Peggy; Stephens, Robert L; Croskey, Adrienne

2016-10-01

Emerging adults who receive services based on positive youth development models have shown an ability to shape their own life course to achieve positive goals. This paper reports secondary data analysis from the Longitudinal Child and Family Outcome Study including 248 culturally diverse youth ages 17 through 22 receiving mental health services in systems of care. After 12 months of services, school performance was positively related to youth ratings of school functioning and service participation and satisfaction. Regression analysis revealed ratings of young peoples' perceptions of school functioning, and their experience in services added to the significant prediction of satisfactory school performance, even controlling for sex and attendance. Finally, in addition to expected predictors, participation in planning their own services significantly predicted enrollment in higher education for those who finished high school. Findings suggest that programs and practices based on positive youth development approaches can improve educational outcomes for emerging adults.
Concerted down-regulation of immune-system related genes predicts metastasis in colorectal carcinoma

International Nuclear Information System (INIS)

Fehlker, Marion; Huska, Matthew R; Jöns, Thomas; Andrade-Navarro, Miguel A; Kemmner, Wolfgang

2014-01-01

This study aimed at the identification of prognostic gene expression markers in early primary colorectal carcinomas without metastasis at the time point of surgery by analyzing genome-wide gene expression profiles using oligonucleotide microarrays. Cryo-conserved tumor specimens from 45 patients with early colorectal cancers were examined, with the majority of them being UICC stage II or earlier and with a follow-up time of 41–115 months. Gene expression profiling was performed using Whole Human Genome 4x44K Oligonucleotide Microarrays. Validation of microarray data was performed on five of the genes in a smaller cohort. Using a novel algorithm based on the recursive application of support vector machines (SVMs), we selected a signature of 44 probes that discriminated between patients developing later metastasis and patients with a good prognosis. Interestingly, almost half of the genes was related to the patients’ immune response and showed reduced expression in the metastatic cases. Whereas up to now gene signatures containing genes with various biological functions have been described for prediction of metastasis in CRC, in this study metastasis could be well predicted by a set of gene expression markers consisting exclusively of genes related to the MHC class II complex involved in immune response. Thus, our data emphasize that the proper function of a comprehensive network of immune response genes is of vital importance for the survival of colorectal cancer patients
Outcome Prediction in Mathematical Models of Immune Response to Infection.

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Manuel Mai

Full Text Available Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.
ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

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Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

2016-11-01

ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P DRAGON score estimates (P DRAGON score estimates (P DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.
Neurophysiological prediction of neurological good and poor outcome in post-anoxic coma.

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Grippo, A; Carrai, R; Scarpino, M; Spalletti, M; Lanzo, G; Cossu, C; Peris, A; Valente, S; Amantini, A

2017-06-01

Investigation of the utility of association between electroencephalogram (EEG) and somatosensory-evoked potentials (SEPs) for the prediction of neurological outcome in comatose patients resuscitated after cardiac arrest (CA) treated with therapeutic hypothermia, according to different recording times after CA. Glasgow Coma Scale, EEG and SEPs performed at 12, 24 and 48-72 h after CA were assessed in 200 patients. Outcome was evaluated by Cerebral Performance Category 6 months after CA. Within 12 h after CA, grade 1 EEG predicted good outcome and bilaterally absent (BA) SEPs predicted poor outcome. Because grade 1 EEG and BA-SEPs were never found in the same patient, the recording of both EEG and SEPs allows us to correctly prognosticate a greater number of patients with respect to the use of a single test within 12 h after CA. At 48-72 h after CA, both grade 2 EEG and BA-SEPs predicted poor outcome with FPR=0.0%. When these neurophysiological patterns are both present in the same patient, they confirm and strengthen their prognostic value, but because they also occurred independently in eight patients, poor outcome is predictable in a greater number of patients. The combination of EEG/SEP findings allows prediction of good and poor outcome (within 12 h after CA) and of poor outcome (after 48-72 h). Recording of EEG and SEPs in the same patients allows always an increase in the number of cases correctly classified, and an increase of the reliability of prognostication in a single patient due to concordance of patterns. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

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Ettore Mosca

2017-09-01

Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.
What Factors are Predictive of Patient-reported Outcomes? A Prospective Study of 337 Shoulder Arthroplasties.

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Matsen, Frederick A; Russ, Stacy M; Vu, Phuong T; Hsu, Jason E; Lucas, Robert M; Comstock, Bryan A

2016-11-01

a better outcome). A cutoff probability of 0.68 yielded the best performance of the model with cross-validation predictions of better outcomes for 236 patients (80%) and worse outcomes for 58 patients (20%); sensitivity of 91% (95% CI, 88%-95%); specificity of 65% (95% CI, 53%-77%); positive predictive value of 92% (95% CI, 88%-95%); and negative predictive value of 64% (95% CI, 51%-76%). We found six easy-to-determine preoperative patient and shoulder factors that were significantly associated with better outcomes of shoulder arthroplasty. A model based on these characteristics had good predictive properties for identifying patients likely to have a better outcome from shoulder arthroplasty. Future research could refine this model with larger patient populations from multiple practices. Level II, therapeutic study.
Androgen receptor profiling predicts prostate cancer outcome

NARCIS (Netherlands)

S. Stelloo (Suzan); E. Nevedomskaya (Ekaterina); H.G. van der Poel (Henk G.); J. de Jong (Jeroen); G.J.H.L. Leenders (Geert); G.W. Jenster (Guido); L. Wessels (Lodewyk); A.M. Bergman (Andries); W. Zwart (Wilbert)

2015-01-01

textabstractProstate cancer is the second most prevalent malignancy in men. Biomarkers for outcome prediction are urgently needed, so that high-risk patients could be monitored more closely postoperatively. To identify prognostic markers and to determine causal players in prostate cancer
Do patient characteristics predict outcome in the outpatient treatment of chronic tinnitus?

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Kröner-Herwig, Birgit; Zachriat, Claudia; Weigand, Doreen

2006-12-06

Various patient characteristics were assessed before offering a treatment to reduce tinnitus related distress to 57 individuals suffering from chronic idiopathic tinnitus. Patients were randomly assigned to a cognitive-behavioral tinnitus coping training (TCT) and a habituation-based training (HT) modelled after Tinnitus Retraining Therapy (TRT) as conceived by Jastreboff. Both trainings were conducted in groups. It was hypothesized that comorbidity regarding mental disorders or psychopathological symptoms (DSM-IV diagnoses, SCL-90R score) and a high level of dysfunctional cognitions relating to tinnitus would have a negative effect on therapy outcome while both trainings proved to be highly efficacious for the average patient. Also further patient features (assessed at baseline) were explored as potential predictors of outcome. None of the hypotheses was corroborated by the data. On the contrary, a higher number of diagnoses was associated with better outcome (statistical trend) and a higher extent of annoyance and interference led to a larger positive change in patients if treated by TCT. No predictor could be identified for long-term success (follow-up ≥18 months) except regarding education. The higher the educational level, the larger was the improvement in HT patients. It is concluded that therapy outcome of TCT and HT can not reliably be predicted by patient characteristics and that early variables of the therapeutic process should be analysed as potentially predicting subsequent therapeutic outcome.
The association of polymorphisms in 5-fluorouracil metabolism genes with outcome in adjuvant treatment of colorectal cancer

DEFF Research Database (Denmark)

Shoaib, Afzal; Gusella, Milena; Jensen, Søren Astrup

2011-01-01

The purpose of this study was to investigate whether specific combinations of polymorphisms in 5-fluorouracil (5-FU) metabolism-related genes were associated with outcome in 5-FU-based adjuvant treatment of colorectal cancer....
A Computational Gene Expression Score for Predicting Immune Injury in Renal Allografts.

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Tara K Sigdel

Full Text Available Whole genome microarray meta-analyses of 1030 kidney, heart, lung and liver allograft biopsies identified a common immune response module (CRM of 11 genes that define acute rejection (AR across different engrafted tissues. We evaluated if the CRM genes can provide a molecular microscope to quantify graft injury in acute rejection (AR and predict risk of progressive interstitial fibrosis and tubular atrophy (IFTA in histologically normal kidney biopsies.Computational modeling was done on tissue qPCR based gene expression measurements for the 11 CRM genes in 146 independent renal allografts from 122 unique patients with AR (n = 54 and no-AR (n = 92. 24 demographically matched patients with no-AR had 6 and 24 month paired protocol biopsies; all had histologically normal 6 month biopsies, and 12 had evidence of progressive IFTA (pIFTA on their 24 month biopsies. Results were correlated with demographic, clinical and pathology variables.The 11 gene qPCR based tissue CRM score (tCRM was significantly increased in AR (5.68 ± 0.91 when compared to STA (1.29 ± 0.28; p < 0.001 and pIFTA (7.94 ± 2.278 versus 2.28 ± 0.66; p = 0.04, with greatest significance for CXCL9 and CXCL10 in AR (p <0.001 and CD6 (p<0.01, CXCL9 (p<0.05, and LCK (p<0.01 in pIFTA. tCRM was a significant independent correlate of biopsy confirmed AR (p < 0.001; AUC of 0.900; 95% CI = 0.705-903. Gene expression modeling of 6 month biopsies across 7/11 genes (CD6, INPP5D, ISG20, NKG7, PSMB9, RUNX3, and TAP1 significantly (p = 0.037 predicted the development of pIFTA at 24 months.Genome-wide tissue gene expression data mining has supported the development of a tCRM-qPCR based assay for evaluating graft immune inflammation. The tCRM score quantifies injury in AR and stratifies patients at increased risk of future pIFTA prior to any perturbation of graft function or histology.
Integrative analysis of survival-associated gene sets in breast cancer.

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Varn, Frederick S; Ung, Matthew H; Lou, Shao Ke; Cheng, Chao

2015-03-12

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used
Comparison of models for predicting outcomes in patients with coronary artery disease focusing on microsimulation

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Masoud Amiri

2012-01-01

Full Text Available Background: Physicians have difficulty to subjectively estimate the cardiovascular risk of their patients. Using an estimate of global cardiovascular risk could be more relevant to guide decisions than using binary representation (presence or absence of risk factors data. The main aim of the paper is to compare different models of predicting the progress of a coronary artery diseases (CAD to help the decision making of physician. Methods: There are different standard models for predicting risk factors such as models based on logistic regression model, Cox regression model, dynamic logistic regression model, and simulation models such as Markov model and microsimulation model. Each model has its own application which can or cannot use by physicians to make a decision on treatment of each patient. Results: There are five main common models for predicting of outcomes, including models based on logistic regression model (for short-term outcomes, Cox regression model (for intermediate-term outcomes, dynamic logistic regression model, and simulation models such as Markov and microsimulation models (for long-term outcomes. The advantages and disadvantages of these models have been discussed and summarized. Conclusion: Given the complex medical decisions that physicians face in everyday practice, the multiple interrelated factors that play a role in choosing the optimal treatment, and the continuously accumulating new evidence on determinants of outcome and treatment options for CAD, physicians may potentially benefit from a clinical decision support system that accounts for all these considerations. The microsimulation model could provide cardiologists, researchers, and medical students a user-friendly software, which can be used as an intelligent interventional simulator.
A simple score to predict fetal outcomes in gestational diabetes mellitus

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Kushal Naha

2015-04-01

Full Text Available Background: Strict glycemic control is critical in preventing adverse maternal and fetal outcomes with gestational diabetes mellitus (GDM, but frequently results in recurrent maternal hypoglycemia and is often impracticable. This study was done to determine whether a more lenient strategy might provide satisfactory outcomes and to formulate a glycemic score for prognostication of fetal outcomes. Methods: A prospective non-interventional study was conducted on consecutive patients admitted with GDM between May 2007 and August 2009. Patients with pre-gestational diabetes were excluded. All patients received treatment at the discretion of treating consultants. Glycemic control was estimated by recording mean values of all glucose profiles performed. Fasting and postprandial blood glucose levels below 95 mg/dl and 120 mg/dl, respectively, were considered controlled. A glycemic score was calculated based on the number of mean blood glucose values controlled. Fetal outcomes were noted. Results: Ninety-four patients with GDM were included. The glycemic score was significantly predictive of adverse fetal outcomes (p < 0.001. Analysis by receiver operating characteristic (ROC curve showed good sensitivity and specificity for macrosomia (78.3% and 81.8%, respectively and congenital anomalies (73.9% and 66.7%, respectively with a glycemic score of 2 or less [area under curve (AUC 0.768; odds ratio (OR, 11.17; 95% Confidence Interval (CI, 2.58-48.35; p < 0.001; and AUC 0.765; OR, 2.22; 95% CI, 0.71-6.92; p = 0.055, respectively]. Binomial logistic regression confirmed the glycemic score to be independently predictive of fetal outcome (p = 0.015. Conclusion: The glycemic score is a sensitive and specific prognostic marker. Tight control of three of four values of blood glucose within the glucose profile appears sufficient to prevent adverse fetal outcomes.

Genome sequence analysis of predicted polyprenol reductase gene from mangrove plant kandelia obovata

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Basyuni, M.; Sagami, H.; Baba, S.; Oku, H.

2018-03-01

It has been previously reported that dolichols but not polyprenols were predominated in mangrove leaves and roots. Therefore, the occurrence of larger amounts of dolichol in leaves of mangrove plants implies that polyprenol reductase is responsible for the conversion of polyprenol to dolichol may be active in mangrove leaves. Here we report the early assessment of probably polyprenol reductase gene from genome sequence of mangrove plant Kandelia obovata. The functional assignment of the gene was based on a homology search of the sequences against the non-redundant (nr) peptide database of NCBI using Blastx. The degree of sequence identity between DNA sequence and known polyprenol reductase was confirmed using the Blastx probability E-value, total score, and identity. The genome sequence data resulted in three partial sequences, termed c23157 (700 bp), c23901 (960 bp), and c24171 (531 bp). The c23157 gene showed the highest similarity (61%) to predicted polyprenol reductase 2- like from Gossypium raimondii with E-value 2e-100. The second gene was c23901 to exhibit high similarity (78%) to the steroid 5-alpha-reductase Det2 from J. curcas with E-value 2e-140. Furthermore, the c24171 gene depicted highest similarity (79%) to the polyprenol reductase 2 isoform X1 from Jatropha curcas with E- value 7e-21.The present study suggested that the c23157, c23901, and c24171, genes may encode predicted polyprenol reductase. The c23157, c23901, c24171 are therefore the new type of predicted polyprenol reductase from K. obovata.
Do personality traits predict outcome of psychodynamically oriented psychosomatic inpatient treatment beyond initial symptoms?

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Steinert, Christiane; Klein, Susanne; Leweke, Frank; Leichsenring, Falk

2015-03-01

outcome, the domains tapped by the NEO-FFI reflect current psychological symptomatology in inpatients with depressive, anxiety, stress or somatoform disorders. From a clinician's point of view monitoring individual progress by using actuarial measures is more valuable than trying to predict who will benefit from treatment using personality assessments. Limitations of the study Diagnostic assessment was solely based on clinical evaluation rather than structured interviews. Twenty-five per cent of the original sample had to be excluded due to missing data. There was a focus on only one set of client characteristics (i.e., five-factor model personality traits). Assessment of personality domains in the acute phase of a mental disorder may be problematic and could have influenced findings. © 2014 The British Psychological Society.
Evaluation of CROES Nephrolithometry Nomogram as a Preoperative Predictive System for Percutaneous Nephrolithotomy Outcomes.

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Kumar, Sumit; Sreenivas, Jayaram; Karthikeyan, Vilvapathy Senguttuvan; Mallya, Ashwin; Keshavamurthy, Ramaiah

2016-10-01

Scoring systems have been devised to predict outcomes of percutaneous nephrolithotomy (PCNL). CROES nephrolithometry nomogram (CNN) is the latest tool devised to predict stone-free rate (SFR). We aim to compare predictive accuracy of CNN against Guy stone score (GSS) for SFR and postoperative outcomes. Between January 2013 and December 2015, 313 patients undergoing PCNL were analyzed for predictive accuracy of GSS, CNN, and stone burden (SB) for SFR, complications, operation time (OT), and length of hospitalization (LOH). We further stratified patients into risk groups based on CNN and GSS. Mean ± standard deviation (SD) SB was 298.8 ± 235.75 mm 2 . SB, GSS, and CNN (area under curve [AUC]: 0.662, 0.660, 0.673) were found to be predictors of SFR. However, predictability for complications was not as good (AUC: SB 0.583, GSS 0.554, CNN 0.580). Single implicated calix (Adj. OR 3.644; p = 0.027), absence of staghorn calculus (Adj. OR 3.091; p = 0.044), single stone (Adj. OR 3.855; p = 0.002), and single puncture (Adj. OR 2.309; p = 0.048) significantly predicted SFR on multivariate analysis. Charlson comorbidity index (CCI; p = 0.020) and staghorn calculus (p = 0.002) were independent predictors for complications on linear regression. SB and GSS independently predicted OT on multivariate analysis. SB and complications significantly predicted LOH, while GSS and CNN did not predict LOH. CNN offered better risk stratification for residual stones than GSS. CNN and GSS have good preoperative predictive accuracy for SFR. Number of implicated calices may affect SFR, and CCI affects complications. Studies should incorporate these factors in scoring systems and assess if predictability of PCNL outcomes improves.
Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

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Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

2017-10-03

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.
Information dimension analysis of bacterial essential and nonessential genes based on chaos game representation

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Zhou, Qian; Yu, Yong-ming

2014-01-01

Essential genes are indispensable for the survival of an organism. Investigating features associated with gene essentiality is fundamental to the prediction and identification of the essential genes. Selecting features associated with gene essentiality is fundamental to predict essential genes with computational techniques. We use fractal theory to make comparative analysis of essential and nonessential genes in bacteria. The information dimensions of essential genes and nonessential genes available in the DEG database for 27 bacteria are calculated based on their gene chaos game representations (CGRs). It is found that weak positive linear correlation exists between information dimension and gene length. Moreover, for genes of similar length, the average information dimension of essential genes is larger than that of nonessential genes. This indicates that essential genes show less regularity and higher complexity than nonessential genes. Our results show that for bacterium with a similar number of essential genes and nonessential genes, the CGR information dimension is helpful for the classification of essential genes and nonessential genes. Therefore, the gene CGR information dimension is very probably a useful gene feature for a genetic algorithm predicting essential genes. (paper)
Reliability of computerized cephalometric outcome predictions of mandibular set-back surgery

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Stefanović Neda

2011-01-01

Full Text Available Introduction. A successful treatment outcome in dentofacial deformity patients commonly requires combined orthodontic-surgical therapy. This enables us to overcome functional, aesthetic and psychological problems. Since most patients state aesthetics as the primary motive for seeking therapy, cephalometric predictions of treatment outcome have become the essential part of treatment planning, especially in combined orthodontic-surgical cases. Objective. The aim of this study was to evaluate the validity and reliability of computerized orthognathic surgery outcome predictions generated using the Nemotec Dental Studio NX 2005 software. Methods. The sample of the study consisted of 31 patients diagnosed with mandibular prognathism who were surgically treated at the Hospital for Maxillofacial Surgery in Belgrade. Investigation was done on lateral cephalograms made before and after surgical treatment. Cephalograms were digitized and analyzed using computer software. According to measurements made on superimposed pre- and postsurgical cephalograms, the patients were retreated within the software and the predictions were assessed by measuring seven angular and three linear parameters. Prediction measurements were then compared with the actual outcome. Results. Results showed statistically significant changes between posttreatment and predicted values for parameters referring to lower lip and mentolabial sulcus position. Conclusion. Computerized cephalometric predictions for hard-tissue structures in the sagittal and vertical planes, as well as the VTO parameters, generated using the Nemotec Dental Studio NX 2005 software are reliable, while lower lip and mentolabial sulcus position predictions are not reliable enough.
A 7 gene expression score predicts for radiation response in cancer cervix

International Nuclear Information System (INIS)

Rajkumar, Thangarajan; Vijayalakshmi, Neelakantan; Sabitha, Kesavan; Shirley, Sundersingh; Selvaluxmy, Ganesharaja; Bose, Mayil Vahanan; Nambaru, Lavanya

2009-01-01

Cervical cancer is the most common cancer among Indian women. The current recommendations are to treat the stage IIB, IIIA, IIIB and IVA with radical radiotherapy and weekly cisplatin based chemotherapy. However, Radiotherapy alone can help cure more than 60% of stage IIB and up to 40% of stage IIIB patients. Archival RNA samples from 15 patients who had achieved complete remission and stayed disease free for more than 36 months (No Evidence of Disease or NED group) and 10 patients who had failed radical radiotherapy (Failed group) were included in the study. The RNA were amplified, labelled and hybridized to Stanford microarray chips and analyzed using BRB Array Tools software and Significance Analysis of Microarray (SAM) analysis. 20 genes were selected for further validation using Relative Quantitation (RQ) Taqman assay in a Taqman Low-Density Array (TLDA) format. The RQ value was calculated, using each of the NED sample once as a calibrator. A scoring system was developed based on the RQ value for the genes. Using a seven gene based scoring system, it was possible to distinguish between the tumours which were likely to respond to the radiotherapy and those likely to fail. The mean score ± 2 SE (standard error of mean) was used and at a cut-off score of greater than 5.60, the sensitivity, specificity, Positive predictive value (PPV) and Negative predictive value (NPV) were 0.64, 1.0, 1.0, 0.67, respectively, for the low risk group. We have identified a 7 gene signature which could help identify patients with cervical cancer who can be treated with radiotherapy alone. However, this needs to be validated in a larger patient population
Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

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Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

2013-09-22

High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.
Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury

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Gary E Strangman

2010-10-01

Full Text Available Cognitive deficits following traumatic brain injury (TBI commonly include difficulties with memory, attention, and executive dysfunction. These deficits are amenable to cognitive rehabilitation, but optimally selecting rehabilitation programs for individual patients remains a challenge. Recent methods for quantifying regional brain morphometry allow for automated quantification of tissue volumes in numerous distinct brain structures. We hypothesized that such quantitative structural information could help identify individuals more or less likely to benefit from memory rehabilitation. Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning. They then participated in a 12 session memory rehabilitation program emphasizing internal memory strategies (I-MEMS. Primary outcome measures (HVLT, RBMT were collected at the time of the MRI scan, immediately following therapy, and again at one month post-therapy. Regional brain volumes were used to predict outcome, adjusting for standard predictors (e.g., injury severity, age, education, pretest scores. We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex. The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable. We conclude that specific cerebral networks including these regions may contribute to learning during I-MEMS rehabilitation, and suggest that morphometric measures may provide substantial predictive value for rehabilitation outcome in other cognitive interventions as well.
A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients

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Fardin Paolo

2010-07-01

Full Text Available Abstract Background Hypoxia is a condition of low oxygen tension occurring in the tumor microenvironment and it is related to poor prognosis in human cancer. To examine the relationship between hypoxia and neuroblastoma, we generated and tested an in vitro derived hypoxia gene signature for its ability to predict patients' outcome. Results We obtained the gene expression profile of 11 hypoxic neuroblastoma cell lines and we derived a robust 62 probesets signature (NB-hypo taking advantage of the strong discriminating power of the l1-l2 feature selection technique combined with the analysis of differential gene expression. We profiled gene expression of the tumors of 88 neuroblastoma patients and divided them according to the NB-hypo expression values by K-means clustering. The NB-hypo successfully stratifies the neuroblastoma patients into good and poor prognosis groups. Multivariate Cox analysis revealed that the NB-hypo is a significant independent predictor after controlling for commonly used risk factors including the amplification of MYCN oncogene. NB-hypo increases the resolution of the MYCN stratification by dividing patients with MYCN not amplified tumors in good and poor outcome suggesting that hypoxia is associated with the aggressiveness of neuroblastoma tumor independently from MYCN amplification. Conclusions Our results demonstrate that the NB-hypo is a novel and independent prognostic factor for neuroblastoma and support the view that hypoxia is negatively correlated with tumors' outcome. We show the power of the biology-driven approach in defining hypoxia as a critical molecular program in neuroblastoma and the potential for improvement in the current criteria for risk stratification.
A relative variation-based method to unraveling gene regulatory networks.

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Yali Wang

Full Text Available Gene regulatory network (GRN reconstruction is essential in understanding the functioning and pathology of a biological system. Extensive models and algorithms have been developed to unravel a GRN. The DREAM project aims to clarify both advantages and disadvantages of these methods from an application viewpoint. An interesting yet surprising observation is that compared with complicated methods like those based on nonlinear differential equations, etc., methods based on a simple statistics, such as the so-called Z-score, usually perform better. A fundamental problem with the Z-score, however, is that direct and indirect regulations can not be easily distinguished. To overcome this drawback, a relative expression level variation (RELV based GRN inference algorithm is suggested in this paper, which consists of three major steps. Firstly, on the basis of wild type and single gene knockout/knockdown experimental data, the magnitude of RELV of a gene is estimated. Secondly, probability for the existence of a direct regulation from a perturbed gene to a measured gene is estimated, which is further utilized to estimate whether a gene can be regulated by other genes. Finally, the normalized RELVs are modified to make genes with an estimated zero in-degree have smaller RELVs in magnitude than the other genes, which is used afterwards in queuing possibilities of the existence of direct regulations among genes and therefore leads to an estimate on the GRN topology. This method can in principle avoid the so-called cascade errors under certain situations. Computational results with the Size 100 sub-challenges of DREAM3 and DREAM4 show that, compared with the Z-score based method, prediction performances can be substantially improved, especially the AUPR specification. Moreover, it can even outperform the best team of both DREAM3 and DREAM4. Furthermore, the high precision of the obtained most reliable predictions shows that the suggested algorithm may be
Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

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Sharafoddini, Anis; Dubin, Joel A

2017-01-01

Background Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health
Chronic Kidney Disease – Where Next? Predicting Outcomes and Planning Care Pathways

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Angharad Marks

2014-07-01

Full Text Available With the introduction of the National Kidney Foundation Kidney Disease Outcomes Quality Initiative chronic kidney disease (CKD guidelines, CKD has been identified as common, particularly in the elderly. The outcomes for those with CKD can be poor: mortality, initiation of renal replacement therapy, and progressive deterioration in kidney function, with its associated complications. In young people with CKD, the risk of poor outcome is high and the social cost substantial, but the actual number of patients affected is relatively small. In the elderly, the risk of poor outcome is substantially lower, but due to the high prevalence of CKD the actual number of poor outcomes attributable to CKD is higher. Predicting which patients are at greatest risk, and being able to tailor care appropriately, has significant potential benefits. Risk prediction models in CKD are being developed and show promise but thus far have limitations. In this review we describe the pathway for developing and evaluating risk prediction tools, and consider what models we have for CKD prediction and where next.
Network-based prediction and analysis of HIV dependency factors.

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T M Murali

2011-09-01

Full Text Available HIV Dependency Factors (HDFs are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.
CORECLUST: identification of the conserved CRM grammar together with prediction of gene regulation.

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Nikulova, Anna A; Favorov, Alexander V; Sutormin, Roman A; Makeev, Vsevolod J; Mironov, Andrey A

2012-07-01

Identification of transcriptional regulatory regions and tracing their internal organization are important for understanding the eukaryotic cell machinery. Cis-regulatory modules (CRMs) of higher eukaryotes are believed to possess a regulatory 'grammar', or preferred arrangement of binding sites, that is crucial for proper regulation and thus tends to be evolutionarily conserved. Here, we present a method CORECLUST (COnservative REgulatory CLUster STructure) that predicts CRMs based on a set of positional weight matrices. Given regulatory regions of orthologous and/or co-regulated genes, CORECLUST constructs a CRM model by revealing the conserved rules that describe the relative location of binding sites. The constructed model may be consequently used for the genome-wide prediction of similar CRMs, and thus detection of co-regulated genes, and for the investigation of the regulatory grammar of the system. Compared with related methods, CORECLUST shows better performance at identification of CRMs conferring muscle-specific gene expression in vertebrates and early-developmental CRMs in Drosophila.
PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy.

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Boyd, Roslyn N; Davies, Peter Sw; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Sakzewski, Leanne; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-07-12

Cerebral palsy (CP) remains the world's most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8-12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006-2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5-5 then 8-12 years of direct clinical assessment to enable prediction of outcomes
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

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Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana.

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Zhang, Weixiong; Ruan, Jianhua; Ho, Tuan-Hua David; You, Youngsook; Yu, Taotao; Quatrano, Ralph S

2005-07-15

A fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. This problem can be referred to as targeted gene finding. Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding target genes. A viable approach to targeted gene finding is to exploit the cis-regulatory elements that are known to be responsible for the transcription of target genes. Given such cis-elements, putative target genes whose promoters contain the elements can be identified. As a case study, we apply the above approach to predict the genes in model plant Arabidopsis thaliana which are inducible by a phytohormone, abscisic acid (ABA), and abiotic stress, such as drought, cold and salinity. We first construct and analyze two ABA specific cis-elements, ABA-responsive element (ABRE) and its coupling element (CE), in A.thaliana, based on their conservation in rice and other cereal plants. We then use the ABRE-CE module to identify putative ABA-responsive genes in A.thaliana. Based on RT-PCR verification and the results from literature, this method has an accuracy rate of 67.5% for the top 40 predictions. The cis-element based targeted gene finding approach is expected to be widely applicable since a large number of cis-elements in many species are available.
A statistical method for predicting splice variants between two groups of samples using GeneChip® expression array data

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Olson James M

2006-04-01

Full Text Available Abstract Background Alternative splicing of pre-messenger RNA results in RNA variants with combinations of selected exons. It is one of the essential biological functions and regulatory components in higher eukaryotic cells. Some of these variants are detectable with the Affymetrix GeneChip® that uses multiple oligonucleotide probes (i.e. probe set, since the target sequences for the multiple probes are adjacent within each gene. Hybridization intensity from a probe correlates with abundance of the corresponding transcript. Although the multiple-probe feature in the current GeneChip® was designed to assess expression values of individual genes, it also measures transcriptional abundance for a sub-region of a gene sequence. This additional capacity motivated us to develop a method to predict alternative splicing, taking advance of extensive repositories of GeneChip® gene expression array data. Results We developed a two-step approach to predict alternative splicing from GeneChip® data. First, we clustered the probes from a probe set into pseudo-exons based on similarity of probe intensities and physical adjacency. A pseudo-exon is defined as a sequence in the gene within which multiple probes have comparable probe intensity values. Second, for each pseudo-exon, we assessed the statistical significance of the difference in probe intensity between two groups of samples. Differentially expressed pseudo-exons are predicted to be alternatively spliced. We applied our method to empirical data generated from GeneChip® Hu6800 arrays, which include 7129 probe sets and twenty probes per probe set. The dataset consists of sixty-nine medulloblastoma (27 metastatic and 42 non-metastatic samples and four cerebellum samples as normal controls. We predicted that 577 genes would be alternatively spliced when we compared normal cerebellum samples to medulloblastomas, and predicted that thirteen genes would be alternatively spliced when we compared metastatic
Prediction of the damage-associated non-synonymous single nucleotide polymorphisms in the human MC1R gene.

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Hepp, Diego; Gonçalves, Gislene Lopes; de Freitas, Thales Renato Ochotorena

2015-01-01

The melanocortin 1 receptor (MC1R) is involved in the control of melanogenesis. Polymorphisms in this gene have been associated with variation in skin and hair color and with elevated risk for the development of melanoma. Here we used 11 computational tools based on different approaches to predict the damage-associated non-synonymous single nucleotide polymorphisms (nsSNPs) in the coding region of the human MC1R gene. Among the 92 nsSNPs arranged according to the predictions 62% were classified as damaging in more than five tools. The classification was significantly correlated with the scores of two consensus programs. Alleles associated with the red hair color (RHC) phenotype and with the risk of melanoma were examined. The R variants D84E, R142H, R151C, I155T, R160W and D294H were classified as damaging by the majority of the tools while the r variants V60L, V92M and R163Q have been predicted as neutral in most of the programs The combination of the prediction tools results in 14 nsSNPs indicated as the most damaging mutations in MC1R (L48P, R67W, H70Y, P72L, S83P, R151H, S172I, L206P, T242I, G255R, P256S, C273Y, C289R and R306H); C273Y showed to be highly damaging in SIFT, Polyphen-2, MutPred, PANTHER and PROVEAN scores. The computational analysis proved capable of identifying the potentially damaging nsSNPs in MC1R, which are candidates for further laboratory studies of the functional and pharmacological significance of the alterations in the receptor and the phenotypic outcomes.

Prediction of the damage-associated non-synonymous single nucleotide polymorphisms in the human MC1R gene.

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Diego Hepp

Full Text Available The melanocortin 1 receptor (MC1R is involved in the control of melanogenesis. Polymorphisms in this gene have been associated with variation in skin and hair color and with elevated risk for the development of melanoma. Here we used 11 computational tools based on different approaches to predict the damage-associated non-synonymous single nucleotide polymorphisms (nsSNPs in the coding region of the human MC1R gene. Among the 92 nsSNPs arranged according to the predictions 62% were classified as damaging in more than five tools. The classification was significantly correlated with the scores of two consensus programs. Alleles associated with the red hair color (RHC phenotype and with the risk of melanoma were examined. The R variants D84E, R142H, R151C, I155T, R160W and D294H were classified as damaging by the majority of the tools while the r variants V60L, V92M and R163Q have been predicted as neutral in most of the programs The combination of the prediction tools results in 14 nsSNPs indicated as the most damaging mutations in MC1R (L48P, R67W, H70Y, P72L, S83P, R151H, S172I, L206P, T242I, G255R, P256S, C273Y, C289R and R306H; C273Y showed to be highly damaging in SIFT, Polyphen-2, MutPred, PANTHER and PROVEAN scores. The computational analysis proved capable of identifying the potentially damaging nsSNPs in MC1R, which are candidates for further laboratory studies of the functional and pharmacological significance of the alterations in the receptor and the phenotypic outcomes.
Predicting outcome from coma : man-in-the-barrel syndrome as potential pitfall

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Elting, JW; Haaxma, R; De Keyser, J; Sulter, G.

The Glasgow coma scale motor score is often used in predicting outcome after hypoxic ischemic coma. Judicious care should be exerted when using this variable in predicting outcome in patients with coma following hypotension since borderzone infarction can obscure the clinical picture. We describe a
Automatic stimulation of experiments and learning based on prediction failure recognition

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Juarez Cordova, A.G.; Kahl, B.; Henne, T.; Prassler, E.

2009-01-01

In this paper we focus on the task of automatically and autonomously initiating experimentation and learning based on the recognition of prediction failure. We present a mechanism that utilizes conceptual knowledge to predict the outcome of robot actions, observes their execution and indicates when
Prediction of motor outcomes and activities of daily living function using diffusion tensor tractography in acute hemiparetic stroke patients.

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Imura, Takeshi; Nagasawa, Yuki; Inagawa, Tetsuji; Imada, Naoki; Izumi, Hiroaki; Emoto, Katsuya; Tani, Itaru; Yamasaki, Hiroyuki; Ota, Yuichiro; Oki, Shuichi; Maeda, Tadanori; Araki, Osamu

2015-05-01

[Purpose] The efficacy of diffusion tensor imaging in the prediction of motor outcomes and activities of daily living function remains unclear. We evaluated the most appropriate diffusion tensor parameters and methodology to determine whether the region of interest- or tractography-based method was more useful for predicting motor outcomes and activities of daily living function in stroke patients. [Subjects and Methods] Diffusion tensor imaging data within 10 days after stroke onset were collected and analyzed for 25 patients. The corticospinal tract was analyzed. Fractional anisotropy, number of fibers, and apparent diffusion coefficient were used as diffusion tensor parameters. Motor outcomes and activities of daily living function were evaluated on the same day as diffusion tensor imaging and at 1 month post-onset. [Results] The fractional anisotropy value of the affected corticospinal tract significantly correlated with the motor outcome and activities of daily living function within 10 days post-onset and at 1 month post-onset. Tthere were no significant correlations between other diffusion tensor parameters and motor outcomes or activities of daily living function. [Conclusion] The fractional anisotropy value of the affected corticospinal tract obtained using the tractography-based method was useful for predicting motor outcomes and activities of daily living function in stroke patients.
Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

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Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

2017-01-01

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384
Omics AnalySIs System for PRecision Oncology (OASISPRO): A Web-based Omics Analysis Tool for Clinical Phenotype Prediction.

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Yu, Kun-Hsing; Fitzpatrick, Michael R; Pappas, Luke; Chan, Warren; Kung, Jessica; Snyder, Michael

2017-09-12

Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. This web-based tool is available at http://tinyurl.com/oasispro ;source codes are available at http://tinyurl.com/oasisproSourceCode . © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

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O'Donoghue, Liza E; Ptitsyn, Andrey A; Kamstock, Debra A; Siebert, Janet; Thomas, Russell S; Duval, Dawn L

2010-01-01

Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for
Gene ontology based transfer learning for protein subcellular localization

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Zhou Shuigeng

2011-02-01

Full Text Available Abstract Background Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the GO terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology. Results In this paper, we propose a Gene Ontology Based Transfer Learning Model (GO-TLM for large-scale protein subcellular localization. The model transfers the signature-based homologous GO terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false GO terms that are resulted from evolutionary divergence. We derive three GO kernels from the three aspects of gene ontology to measure the GO similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for
BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.

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Hoff, Katharina J; Lange, Simone; Lomsadze, Alexandre; Borodovsky, Mark; Stanke, Mario

2016-03-01

Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

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Qihua Tan

2009-01-01

Full Text Available Different from significant gene expression analysis which looks for genes that are differentially regulated, feature selection in the microarray-based prognostic gene expression analysis aims at finding a subset of marker genes that are not only differentially expressed but also informative for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations shows that the feature space can be largely reduced while achieving improved testing performances.
Hopefulness predicts resilience after hereditary colorectal cancer genetic testing: a prospective outcome trajectories study

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Chu Annie TW

2010-06-01

Full Text Available Abstract Background - Genetic testing for hereditary colorectal cancer (HCRC had significant psychological consequences for test recipients. This prospective longitudinal study investigated the factors that predict psychological resilience in adults undergoing genetic testing for HCRC. Methods - A longitudinal study was carried out from April 2003 to August 2006 on Hong Kong Chinese HCRC family members who were recruited and offered genetic testing by the Hereditary Gastrointestinal Cancer Registry to determine psychological outcomes after genetic testing. Self-completed questionnaires were administered immediately before (pre-disclosure baseline and 2 weeks, 4 months and 1 year after result disclosure. Using validated psychological inventories, the cognitive style of hope was measured at baseline, and the psychological distress of depression and anxiety was measured at all time points. Results - Of the 76 participating subjects, 71 individuals (43 men and 28 women; mean age 38.9 ± 9.2 years from nine FAP and 24 HNPCC families completed the study, including 39 mutated gene carriers. Four patterns of outcome trajectories were created using established norms for the specified outcome measures of depression and anxiety. These included chronic dysfunction (13% and 8.7%, recovery (0% and 4.3%, delayed dysfunction (13% and 15.9% and resilience (76.8% and 66.7%. Two logistic regression analyses were conducted using hope at baseline to predict resilience, with depression and anxiety employed as outcome indicators. Because of the small number of participants, the chronic dysfunction and delayed dysfunction groups were combined into a non-resilient group for comparison with the resilient group in all subsequent analysis. Because of low frequencies, participants exhibiting a recovery trajectory (n = 3 for anxiety and n = 0 for depression were excluded from further analysis. Both regression equations were significant. Baseline hope was a significant
A novel mutual information-based Boolean network inference method from time-series gene expression data.

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Shohag Barman

Full Text Available Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.
Plastic Surgeon Expertise in Predicting Breast Reconstruction Outcomes for Patient Decision Analysis

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Clement S. Sun, MS

2013-11-01

Conclusions: The use of individual plastic surgeon–elicited probability information is not encouraged unless the individual’s prediction skill has been evaluated. In the absence of this information, a group consensus on the probability of outcomes is preferred. Without a large evidence base for calculating probabilities, estimates assessed from a group of plastic surgeons may be acceptable for purposes of breast reconstruction decision analysis.
Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.

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Herman F Fumiã

Full Text Available A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns--attractors--dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.
SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes

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Atul Kumar

2017-06-01

Full Text Available Type II diabetes is a chronic condition that affects the way our body metabolizes sugar. The body's important source of fuel is now becoming a chronic disease all over the world. It is now very necessary to identify the new potential targets for the drugs which not only control the disease but also can treat it. Support vector machines are the classifier which has a potential to make a classification of the discriminatory genes and non-discriminatory genes. SVMRFE a modification of SVM ranks the genes based on their discriminatory power and eliminate the genes which are not involved in causing the disease. A gene regulatory network has been formed with the top ranked coding genes to identify their role in causing diabetes. To further validate the results pathway study was performed to identify the involvement of the coding genes in type II diabetes. The genes obtained from this study showed a significant involvement in causing the disease, which may be used as a potential drug target.
A deep learning-based multi-model ensemble method for cancer prediction.

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Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.
Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate

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Gulliford, Sarah L.; Webb, Steve; Rowbottom, Carl G.; Corne, David W.; Dearnaley, David P.

2004-01-01

Background and purpose: This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated. Patients and methods: A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data. Results: It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome. Conclusion: ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes
Relationship between the iceA gene of Helicobacter pylori and clinical outcomes.

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Huang, Xiaojun; Deng, Zhaomin; Zhang, Qiang; Li, Wanyi; Wang, Baoning; Li, Mingyuan

2016-01-01

The complex pathogenesis of Helicobacter pylori (H. pylori) and the features of the host influence the diverse clinical outcomes. A mass of studies about virulence genes have accelerated the exploration of pathogenesis of H. pylori infection. Induced by contact with epithelium gene A (iceA) is one of the biggest concerned virulence genes. In this study, we explored the relationship between iceA and the magnitude of the risk for clinical outcomes and the prevalence of iceA-positive H. pylori in People's Republic of China and other countries. We searched the electronic databases of PubMed, Embase, CNKI, VIP, and Wanfang by literature search strategy. The studies conforming to the inclusion criteria were assessed. With these data, we systematically analyzed the relationship between the iceA gene of H. pylori and clinical outcomes. Nineteen articles with 22 studies, a total of 2,657 cases, were involved in the study. The iceA1 gene was significantly associated with peptic ulcer disease (odds ratio =1.28, 95% confidence interval =1.03-1.60; P=0.03), especially in People's Republic of China (odds ratio =1.40, 95% confidence interval =1.07-1.83; P=0.01). Moreover, the prevalence of iceA1 was significantly higher than iceA2 in People's Republic of China (P<0.0001). The prevalence of both iceA1 and iceA2 was significantly different (P<0.0001) in People's Republic of China and in other countries. The system analysis showed that infection with the iceA1-positive H. pylori significantly increased the overall risk for peptic ulcer disease, especially in People's Republic of China. The iceA2 gene status and clinical outcome of H. pylori infection have no significant correlation. H. pylori iceA1 genotype is the major epidemic strain in People's Republic of China.
Predicting gene function using hierarchical multi-label decision tree ensembles

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Kocev Dragi

2010-01-01

Full Text Available Abstract Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO. We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Conclusions Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.
Method of tumor volume evaluation using magnetic resonance imaging for outcome prediction in cervical cancer treated with concurrent chemotherapy and radiotherapy

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Kim, Hun Jung; Kim, Woo Chul [Inha University Hospital, Inha University School of Medicine, Seoul (Korea, Republic of)

2012-06-15

To evaluate the patterns of tumor shape and to compare tumor volume derived from simple diameter-based ellipsoid measurement with that derived from tracing the entire tumor contour using region of interest (ROI)-based 3D volumetry with respect to the prediction outcome in cervical cancer patients treated with concurrent chemotherapy and radiotherapy. Magnetic resonance imaging was performed in 98 patients with cervical cancer (stage IB-IIIB). The tumor shape was classified into two categories: ellipsoid and non-ellipsoid shape. ROI-based volumetry was derived from each magnetic resonance slice on the work station. For the diameter-based surrogate 'ellipsoid volume,' the three orthogonal diameters were measured to calculate volume as an ellipsoid. The more than half of tumor (55.1%) had a non-ellipsoid configuration. The predictions for outcome were consistent between two volume groups, with overall survival of 93.6% and 87.7% for small tumor (<20 mL), 62.9% and 69.1% for intermediate-size tumor (20-39 mL), and 14.5% and 16.7% for large tumors ({>=}40 mL) using ROI and diameter based measurement, respectively. Disease-free survival was 93.8% and 90.6% for small tumor, 54.3% and 62.7% for intermediate-size tumor, and 13.7% and 10.3% for large tumor using ROI and diameter based method, respectively. Differences in outcome between size groups were statistically significant, and the differences in outcome predicted by the tumor volume by two different methods. Our data suggested that large numbers of cervical cancers are not ellipsoid. However, simple diameter-based tumor volume measurement appears to be useful in comparison with ROI-based volumetry for predicting outcome in cervical cancer patients.

Method of tumor volume evaluation using magnetic resonance imaging for outcome prediction in cervical cancer treated with concurrent chemotherapy and radiotherapy

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Kim, Hun Jung; Kim, Woo Chul

2012-01-01

To evaluate the patterns of tumor shape and to compare tumor volume derived from simple diameter-based ellipsoid measurement with that derived from tracing the entire tumor contour using region of interest (ROI)-based 3D volumetry with respect to the prediction outcome in cervical cancer patients treated with concurrent chemotherapy and radiotherapy. Magnetic resonance imaging was performed in 98 patients with cervical cancer (stage IB-IIIB). The tumor shape was classified into two categories: ellipsoid and non-ellipsoid shape. ROI-based volumetry was derived from each magnetic resonance slice on the work station. For the diameter-based surrogate 'ellipsoid volume,' the three orthogonal diameters were measured to calculate volume as an ellipsoid. The more than half of tumor (55.1%) had a non-ellipsoid configuration. The predictions for outcome were consistent between two volume groups, with overall survival of 93.6% and 87.7% for small tumor (<20 mL), 62.9% and 69.1% for intermediate-size tumor (20-39 mL), and 14.5% and 16.7% for large tumors (≥40 mL) using ROI and diameter based measurement, respectively. Disease-free survival was 93.8% and 90.6% for small tumor, 54.3% and 62.7% for intermediate-size tumor, and 13.7% and 10.3% for large tumor using ROI and diameter based method, respectively. Differences in outcome between size groups were statistically significant, and the differences in outcome predicted by the tumor volume by two different methods. Our data suggested that large numbers of cervical cancers are not ellipsoid. However, simple diameter-based tumor volume measurement appears to be useful in comparison with ROI-based volumetry for predicting outcome in cervical cancer patients.
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

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2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

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Ching-Hsue Cheng

2018-01-01

Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
Neonatal Sleep-Wake Analyses Predict 18-month Neurodevelopmental Outcomes.

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Shellhaas, Renée A; Burns, Joseph W; Hassan, Fauziya; Carlson, Martha D; Barks, John D E; Chervin, Ronald D

2017-11-01

The neurological examination of critically ill neonates is largely limited to reflexive behavior. The exam often ignores sleep-wake physiology that may reflect brain integrity and influence long-term outcomes. We assessed whether polysomnography and concurrent cerebral near-infrared spectroscopy (NIRS) might improve prediction of 18-month neurodevelopmental outcomes. Term newborns with suspected seizures underwent standardized neurologic examinations to generate Thompson scores and had 12-hour bedside polysomnography with concurrent cerebral NIRS. For each infant, the distribution of sleep-wake stages and electroencephalogram delta power were computed. NIRS-derived fractional tissue oxygen extraction (FTOE) was calculated across sleep-wake stages. At age 18-22 months, surviving participants were evaluated with Bayley Scales of Infant Development (Bayley-III), 3rd edition. Twenty-nine participants completed Bayley-III. Increased newborn time in quiet sleep predicted worse 18-month cognitive and motor scores (robust regression models, adjusted r2 = 0.22, p = .007, and 0.27, .004, respectively). Decreased 0.5-2 Hz electroencephalograph (EEG) power during quiet sleep predicted worse 18-month language and motor scores (adjusted r2 = 0.25, p = .0005, and 0.33, .001, respectively). Predictive values remained significant after adjustment for neonatal Thompson scores or exposure to phenobarbital. Similarly, an attenuated difference in FTOE, between neonatal wakefulness and quiet sleep, predicted worse 18-month cognitive, language, and motor scores in adjusted analyses (each p sleep-as quantified by increased time in quiet sleep, lower electroencephalogram delta power during that stage, and muted differences in FTOE between quiet sleep and wakefulness-may improve prediction of adverse long-term outcomes for newborns with neurological dysfunction. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved
Blood-based gene-expression predictors of PTSD risk and resilience among deployed marines: a pilot study.

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Glatt, Stephen J; Tylee, Daniel S; Chandler, Sharon D; Pazol, Joel; Nievergelt, Caroline M; Woelk, Christopher H; Baker, Dewleen G; Lohr, James B; Kremen, William S; Litz, Brett T; Tsuang, Ming T

2013-06-01

Susceptibility to PTSD is determined by both genes and environment. Similarly, gene-expression levels in peripheral blood are influenced by both genes and environment, and expression levels of many genes show good correspondence between peripheral blood and brain. Therefore, our objectives were to test the following hypotheses: (1) pre-trauma expression levels of a gene subset (particularly immune-system genes) in peripheral blood would differ between trauma-exposed Marines who later developed PTSD and those who did not; (2) a predictive biomarker panel of the eventual emergence of PTSD among high-risk individuals could be developed based on gene expression in readily assessable peripheral blood cells; and (3) a predictive panel based on expression of individual exons would surpass the accuracy of a model based on expression of full-length gene transcripts. Gene-expression levels were assayed in peripheral blood samples from 50 U.S. Marines (25 eventual PTSD cases and 25 non-PTSD comparison subjects) prior to their deployment overseas to war-zones in Iraq or Afghanistan. The panel of biomarkers dysregulated in peripheral blood cells of eventual PTSD cases prior to deployment was significantly enriched for immune genes, achieved 70% prediction accuracy in an independent sample based on the expression of 23 full-length transcripts, and attained 80% accuracy in an independent sample based on the expression of one exon from each of five genes. If the observed profiles of pre-deployment mRNA-expression in eventual PTSD cases can be further refined and replicated, they could suggest avenues for early intervention and prevention among individuals at high risk for trauma exposure. Copyright © 2013 Wiley Periodicals, Inc.
Low plasma bicarbonate predicts poor outcome of cerebral malaria ...

African Journals Online (AJOL)

Malaria remains a major cause of morbidity and mortality in many sub Saharan countries and cerebral malaria is widely recognised as one of its most fatal forms. We studied the predictive value of routine biochemical laboratory indices in predicting the outcome of cerebral malaria in 50 Nigerian children ages 9 months to 6 ...
PRGPred: A platform for prediction of domains of resistance gene analogue (RGA in Arecaceae developed using machine learning algorithms

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MATHODIYIL S. MANJULA

2015-12-01

Full Text Available Plant disease resistance genes (R-genes are responsible for initiation of defense mechanism against various phytopathogens. The majority of plant R-genes are members of very large multi-gene families, which encode structurally related proteins containing nucleotide binding site domains (NBS and C-terminal leucine rich repeats (LRR. Other classes possess' an extracellular LRR domain, a transmembrane domain and sometimes, an intracellular serine/threonine kinase domain. R-proteins work in pathogen perception and/or the activation of conserved defense signaling networks. In the present study, sequences representing resistance gene analogues (RGAs of coconut, arecanut, oil palm and date palm were collected from NCBI, sorted based on domains and assembled into a database. The sequences were analyzed in PRINTS database to find out the conserved domains and their motifs present in the RGAs. Based on these domains, we have also developed a tool to predict the domains of palm R-genes using various machine learning algorithms. The model files were selected based on the performance of the best classifier in training and testing. All these information is stored and made available in the online ‘PRGpred' database and prediction tool.
[Gastroschisis: Prenatal ultrasonography and obstetrical criteria for predicting neonatal outcome].

Science.gov (United States)

Ducellier, G; Moussy, P; Sahmoune, L; Bonneau, S; Alanio, E; Bory, J-P

2016-09-01

Prenatal diagnosis of complex laparoschisis is difficult and yet it is associated with a significantly increased morbidity and mortality. The aim of the study was to define ultrasonographic factor and obstetrical criteria to predicting adverse neonatal outcome. Retrospective cohort study over 10 years, of 35 gastroschisis cases in CHU of Reims (France). The primary outcome was the neonatal death due to gastroschisis. The sonographic markers was bowel dilatation intra- or extra-abdominale, amniotic fluid, intra-uterin growth. The obstetrical criteria was fetal vitality, fetal heart rate, type of delivery, the weight and the term of birth. There were 28 live births, 16 children with favorable outcome, 8 children with adverse perinatal outcome and 4 deaths. There were any sonographic criteria to predicting adverse neonatal outcome. Only the birth weight less than 2000g was associated with an increase gastrointestinal complications (P=0.049). The type of the delivery was not associated with an adverse prenatal outcome. The birth weight less than 2000g seems to be associate with an increase gastrointestinal complications. It is important to fight against prematurity in case of gastroschisis. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
GENECODIS-Grid: An online grid-based tool to predict functional information in gene lists

International Nuclear Information System (INIS)

Nogales, R.; Mejia, E.; Vicente, C.; Montes, E.; Delgado, A.; Perez Griffo, F. J.; Tirado, F.; Pascual-Montano, A.

2007-01-01

In this work we introduce GeneCodis-Grid, a grid-based alternative to a bioinformatics tool named Genecodis that integrates different sources of biological information to search for biological features (annotations) that frequently co-occur in a set of genes and rank them by statistical significance. GeneCodis-Grid is a web-based application that takes advantage of two independent grid networks and a computer cluster managed by a meta-scheduler and a web server that host the application. The mining of concurrent biological annotations provides significant information for the functional analysis of gene list obtained by high throughput experiments in biology. Due to the large popularity of this tool, that has registered more than 13000 visits since its publication in January 2007, there is a strong need to facilitate users from different sites to access the system simultaneously. In addition, the complexity of some of the statistical tests used in this approach has made this technique a good candidate for its implementation in a Grid opportunistic environment. (Author)
No specific gene expression signature in human granulosa and cumulus cells for prediction of oocyte fertilisation and embryo implantation.

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Tanja Burnik Papler

Full Text Available In human IVF procedures objective and reliable biomarkers of oocyte and embryo quality are needed in order to increase the use of single embryo transfer (SET and thus prevent multiple pregnancies. During folliculogenesis there is an intense bi-directional communication between oocyte and follicular cells. For this reason gene expression profile of follicular cells could be an important indicator and biomarker of oocyte and embryo quality. The objective of this study was to identify gene expression signature(s in human granulosa (GC and cumulus (CC cells predictive of successful embryo implantation and oocyte fertilization. Forty-one patients were included in the study and individual GC and CC samples were collected; oocytes were cultivated separately, allowing a correlation with IVF outcome and elective SET was performed. Gene expression analysis was performed using microarrays, followed by a quantitative real-time PCR validation. After statistical analysis of microarray data, there were no significantly differentially expressed genes (FDR<0,05 between non-fertilized and fertilized oocytes and non-implanted and implanted embryos in either of the cell type. Furthermore, the results of quantitative real-time PCR were in consent with microarray data as there were no significant differences in gene expression of genes selected for validation. In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.
Network-based ranking methods for prediction of novel disease associated microRNAs.

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Le, Duc-Hau

2015-10-01

Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In
DNA microarray-based gene expression profiling in diagnosis, assessing prognosis and predicting response to therapy in colorectal cancer

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Przemysław Kwiatkowski

2012-06-01

Full Text Available Colorectal cancer is the most common cancer of the gastrointestinal tract. It is considered as a biological model of a certain type of cancerogenesis process in which progression from an early to late stage adenoma and cancer is accompanied by distinct genetic alterations.Clinical and pathological parameters commonly used in clinical practice are often insufficient to determine groups of patients suitable for personalized treatment. Moreover, reliable molecular markers with high prognostic value have not yet been determined. Molecular studies using DNA-based microarrays have identified numerous genes involved in cell proliferation and differentiation during the process of cancerogenesis. Assessment of the genetic profile of colorectal cancer using the microarray technique might be a useful tool in determining the groups of patients with different clinical outcomes who would benefit from additional personalized treatment.The main objective of this study was to present the current state of knowledge on the practical application of gene profiling techniques using microarrays for determining diagnosis, prognosis and response to treatment in colorectal cancer.
Could the outcome of the 2016 US elections have been predicted from past voting patterns?

Science.gov (United States)

Schmitz, Peter M. U.; Holloway, Jennifer P.; Dudeni-Tlhone, Nontembeko; Ntlangu, Mbulelo B.; Koen, Renee

2018-05-01

In South Africa, a team of analysts has for some years been using statistical techniques to predict election outcomes during election nights in South Africa. The prediction method involves using statistical clusters based on past voting patterns to predict final election outcomes, using a small number of released vote counts. With the US presidential elections in November 2016 hitting the global media headlines during the time period directly after successful predictions were done for the South African elections, the team decided to investigate adapting their meth-od to forecast the final outcome in the US elections. In particular, it was felt that the time zone differences between states would affect the time at which results are released and thereby provide a window of opportunity for doing election night prediction using only the early results from the eastern side of the US. Testing the method on the US presidential elections would have two advantages: it would determine whether the core methodology could be generalised, and whether it would work to include a stronger spatial element in the modelling, since the early results released would be spatially biased due to time zone differences. This paper presents a high-level view of the overall methodology and how it was adapted to predict the results of the US presidential elections. A discussion on the clustering of spatial units within the US is also provided and the spatial distribution of results together with the Electoral College prediction results from both a `test-run' and the final 2016 presidential elections are given and analysed.
Comparing 2 Whiplash Grading Systems to Predict Clinical Outcomes.

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Croft, Arthur C; Bagherian, Alireza; Mickelsen, Patrick K; Wagner, Stephen

2016-06-01

Two whiplash severity grading systems have been developed: Quebec Task Force on Whiplash-Associated Disorders (QTF-WAD) and the Croft grading system. The majority of clinical studies to date have used the modified grading system published by the QTF-WAD in 1995 and have demonstrated some ability to predict outcome. But most studies include only injuries of lower severity (grades 1 and 2), preventing a broader interpretation. The purpose of this study was assess the ability of these grading systems to predict clinical outcome within the context of a broader injury spectrum. This study evaluated both grading systems for their ability to predict the bivalent outcome, recovery, within a sample of 118 whiplash patients who were part of a previous case-control designed study. Of these, 36% (controls) had recovered, and 64% (cases) had not recovered. The discrete bivariate distribution between recovery status and whiplash grade was analyzed using the 2-tailed cross-tabulation statistics. Applying the criteria of the original 1993 Croft grading system, the subset comprised 1 grade 1 injury, 32 grade 2 injuries, 53 grade 3 injuries, and 32 grade 4 injuries. Applying the criteria of the modified (QTF-WAD) grading system, there were 1 grade 1 injury, 89 grade 2 injuries, and 28 grade 3 injuries. Both whiplash grading systems correlated negatively with recovery; that is, higher severity grades predicted a lower probability of recovery, and statistically significant correlations were observed in both, but the Croft grading system substantially outperformed the QTF-WAD system on this measure. The Croft grading system for whiplash injury severity showed a better predictive measure for recovery status from whiplash injuries as compared with the QTF-WAD grading system.
Web-based depression treatment: associations of clients' word use with adherence and outcome.

Science.gov (United States)

Van der Zanden, Rianne; Curie, Keshia; Van Londen, Monique; Kramer, Jeannet; Steen, Gerard; Cuijpers, Pim

2014-05-01

The growing number of web-based psychological treatments, based on textual communication, generates a wealth of data that can contribute to knowledge of online and face-to-face treatments. We investigated whether clients' language use predicted treatment outcomes and adherence in Master Your Mood (MYM), an online group course for young adults with depressive symptoms. Among 234 participants from a randomised controlled trial of MYM, we tested whether their word use on course application forms predicted baseline levels of depression, anxiety and mastery, or subsequent treatment adherence. We then analysed chat session transcripts of course completers (n=67) to investigate whether word use changes predicted changes in treatment outcomes. Depression improvement was predicted by increasing use of 'discrepancy words' during treatment (e.g. should). At baseline, more discrepancy words predicted higher mastery level. Adherence was predicted by more words used at application, more social words and fewer discrepancy words. Many variables were included, increasing the chance of coincidental results. This risk was constrained by examining only those word categories that have been investigated in relation to depression or adherence. This is the first study to link word use during treatment to outcomes of treatment that has proven to be effective in an RCT. The results suggest that paying attention to the length of problem articulation at application and to 'discrepancy words' may be wise, as these seem to be psychological markers. To expand knowledge of word use as psychological marker, research on web-based treatment should include text analysis. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
Tulsa 1000: a naturalistic study protocol for multilevel assessment and outcome prediction in a large psychiatric sample.

Science.gov (United States)

Victor, Teresa A; Khalsa, Sahib S; Simmons, W Kyle; Feinstein, Justin S; Savitz, Jonathan; Aupperle, Robin L; Yeh, Hung-Wen; Bodurka, Jerzy; Paulus, Martin P

2018-01-24

Although neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions. The T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1 year. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. The data will be used to generate computational models based on Bayesian statistics. The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine. Ethical approval was obtained from Western Institutional Review Board screening protocol #20101611. The dissemination plan includes informing health professionals of results for clinical practice, submitting results to journals for peer-reviewed publication, presenting results at national
Comparison of TMS and DTT for predicting motor outcome in intracerebral hemorrhage.

Science.gov (United States)

Jang, Sung Ho; Ahn, Sang Ho; Sakong, Joon; Byun, Woo Mok; Choi, Byung Yun; Chang, Chul Hoon; Bai, Daiseg; Son, Su Min

2010-03-15

TMS (transcranial magnetic stimulation) and DTT (diffusion tensor tractography) have different advantages in evaluating stroke patients. TMS has good clinical accessibility and economical benefit. On the contrary, DTT has a unique advantage to visualize neural tracts three-dimensionally although it requires an expensive and large MRI machine. Many studies have demonstrated that TMS and DTT have predictive values for motor outcome in stroke patients. However, there has been no study on the comparison of these two evaluation tools. In the current study, we compared the abilities of TMS and DTT to predict upper motor outcome in patients with ICH (intracerebral hemorrhage). Fifty-three consecutive patients with severe motor weakness were evaluated by TMS and DTT at the early stage (7-28 days) of ICH. Modified Brunnstrom classification (MBC) and the motricity index of upper extremity (UMI) were evaluated at onset and 6 months after onset. Patients with the presence of a motor evoked potential (MEP) in TMS or a preserved corticospinal tract (CST) in DTT showed better motor outcomes than those without (p=0.000). TMS showed higher positive predictive value than DTT. In contrast, DTT showed higher negative predictive value than TMS. TMS and DTT had different advantages in predicting motor outcome, and this result could be a reference to predict final neurological deficit at the early stage of ICH.
Looking for students'personal characteristics predicting study outcome

NARCIS (Netherlands)

Bergen, T.C.M.; Bragt, van C.A.C.; Bakx, A.W.E.A.; Croon, M.A.

2011-01-01

Abstract The central goal of this study is to clarify to what degree former education and students’ personal characteristics (the ‘Big Five personality characteristics’, personal orientations on learning and students’ study approach) may predict study outcome (required credits and study
Looking for students' personal characteristics predicting study outcome.

NARCIS (Netherlands)

Dr. A. Bakx; Theo Bergen; Dr. Cyrille A.C. Van Bragt; Marcel Croon

2011-01-01

Abstract The central goal of this study is to clarify to what degree former education and students' personal characteristics (the 'Big Five personality characteristics', personal orientations on learning and students' study approach) may predict study outcome (required credits and study
APCR, factor V gene known and novel SNPs and adverse pregnancy outcomes in an Irish cohort of pregnant women

LENUS (Irish Health Repository)

Sedano-Balbas, Sara

2010-03-10

Abstract Background Activated Protein C Resistance (APCR), a poor anticoagulant response of APC in haemostasis, is the commonest heritable thrombophilia. Adverse outcomes during pregnancy have been linked to APCR. This study determined the frequency of APCR, factor V gene known and novel SNPs and adverse outcomes in a group of pregnant women. Methods Blood samples collected from 907 pregnant women were tested using the Coatest® Classic and Modified functional haematological tests to establish the frequency of APCR. PCR-Restriction Enzyme Analysis (PCR-REA), PCR-DNA probe hybridisation analysis and DNA sequencing were used for molecular screening of known mutations in the factor V gene in subjects determined to have APCR based on the Coatest® Classic and\\/or Modified functional haematological tests. Glycosylase Mediated Polymorphism Detection (GMPD), a SNP screening technique and DNA sequencing, were used to identify SNPs in the factor V gene of 5 APCR subjects. Results Sixteen percent of the study group had an APCR phenotype. Factor V Leiden (FVL), FV Cambridge, and haplotype (H) R2 alleles were identified in this group. Thirty-three SNPs; 9 silent SNPs and 24 missense SNPs, of which 20 SNPs were novel, were identified in the 5 APCR subjects. Adverse pregnancy outcomes were found at a frequency of 35% in the group with APCR based on Classic Coatest® test only and at 45% in the group with APCR based on the Modified Coatest® test. Forty-eight percent of subjects with FVL had adverse outcomes while in the group of subjects with no FVL, adverse outcomes occurred at a frequency of 37%. Conclusions Known mutations and novel SNPs in the factor V gene were identified in the study cohort determined to have APCR in pregnancy. Further studies are required to investigate the contribution of these novel SNPs to the APCR phenotype. Adverse outcomes including early pregnancy loss (EPL), preeclampsia (PET) and intrauterine growth restriction (IGUR) were not significantly more

Gene Prediction in Metagenomic Fragments with Deep Learning

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Shao-Wu Zhang

2017-01-01

Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.
Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation

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Shen Li

2010-09-01

Full Text Available Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.
Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

Science.gov (United States)

Basyuni, M.; Wati, R.

2018-03-01

The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.
A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

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Xingli Guo

Full Text Available The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.
Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

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Lemke Ney

2009-09-01

Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing
Predictive value of cognition for different domains of outcome in recent-onset schizophrenia.

Science.gov (United States)

Holthausen, Esther A E; Wiersma, Durk; Cahn, Wiepke; Kahn, René S; Dingemans, Peter M; Schene, Aart H; van den Bosch, Robert J

2007-01-15

The aim of this study was to see whether and how cognition predicts outcome in recent-onset schizophrenia in a large range of domains such as course of illness, self-care, interpersonal functioning, vocational functioning and need for care. At inclusion, 115 recent-onset patients were tested on a cognitive battery and 103 patients participated in the follow-up 2 years after inclusion. Differences in outcome between cognitively normal and cognitively impaired patients were also analysed. Cognitive measures at inclusion did not predict number of relapses, activities of daily living and interpersonal functioning. Time in psychosis or in full remission, as well as need for care, were partly predicted by specific cognitive measures. Although statistically significant, the predictive value of cognition with regard to clinical outcome was limited. There was a significant difference between patients with and without cognitive deficits in competitive employment status and vocational functioning. The predictive value of cognition for different social outcome domains varies. It seems that cognition most strongly predicts work performance, where having a cognitive deficit, regardless of the nature of the deficit, acts as a rate-limiting factor.
PR Interval Associated Genes, Atrial Remodeling and Rhythm Outcome of Catheter Ablation of Atrial Fibrillation—A Gene-Based Analysis of GWAS Data

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Daniela Husser

2017-12-01

Full Text Available Background: PR interval prolongation has recently been shown to associate with advanced left atrial remodeling and atrial fibrillation (AF recurrence after catheter ablation. While different genome-wide association studies (GWAS have implicated 13 loci to associate with the PR interval as an AF endophenotype their subsequent associations with AF remodeling and response to catheter ablation are unknown. Here, we perform a gene-based analysis of GWAS data to test the hypothesis that PR interval candidate genes also associate with left atrial remodeling and arrhythmia recurrence following AF catheter ablation.Methods and Results: Samples from 660 patients with paroxysmal (n = 370 or persistent AF (n = 290 undergoing AF catheter ablation were genotyped for ~1,000,000 SNPs. Gene-based association was investigated using VEGAS (versatile gene-based association study. Among the 13 candidate genes, SLC8A1, MEIS1, ITGA9, SCN5A, and SOX5 associated with the PR interval. Of those, ITGA9 and SOX5 were significantly associated with left atrial low voltage areas and left atrial diameter and subsequently with AF recurrence after radiofrequency catheter ablation.Conclusion: This study suggests contributions of ITGA9 and SOX5 to AF remodeling expressed as PR interval prolongation, low voltage areas and left atrial dilatation and subsequently to response to catheter ablation. Future and larger studies are necessary to replicate and apply these findings with the aim of designing AF pathophysiology-based multi-locus risk scores.
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

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Simpson David

2006-03-01

Full Text Available Abstract Background Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. Results Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE, this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re
A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients.

Science.gov (United States)

Thakar, Sumit; Sivaraju, Laxminadh; Jacob, Kuruthukulangara S; Arun, Aditya Atal; Aryan, Saritha; Mohan, Dilip; Sai Kiran, Narayanam Anantha; Hegde, Alangar S

2018-01-01

fair accuracy in the validation cohort (area under the curve 0.75). CONCLUSIONS The presence of gait imbalance and motor deficits independently predict worse clinical and radiological outcomes, respectively, after decompressive surgery for CMI with altered hindbrain CSF flow. Caudal displacement of the obex and a shorter M-line-FVV distance correlated with good CCOS scores, indicating that patients with a greater degree of hindbrain pathology respond better to surgery. The proposed points-based algorithm has good predictive value for postoperative multifactorial outcome in these patients.
Six-month changes in spirituality and religiousness in alcoholics predict drinking outcomes at nine months.

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Robinson, Elizabeth A R; Krentzman, Amy R; Webb, Jon R; Brower, Kirk J

2011-07-01

Although spiritual change is hypothesized to contribute to recovery from alcohol dependence, few studies have used prospective data to investigate this hypothesis. Prior studies have also been limited to treatment-seeking and Alcoholics Anonymous (AA) samples. This study included alcohol-dependent individuals, both in treatment and not, to investigate the effect of spiritual and religious (SR) change on subsequent drinking outcomes, independent of AA involvement. Alcoholics (N = 364) were recruited for a panel study from two abstinence-based treatment centers, a moderation drinking program, and untreated individuals from the local community. Quantitative measures of SR change between baseline and 6 months were used to predict 9-month drinking outcomes, controlling for baseline drinking and AA involvement. Significant 6-month changes in 8 of 12 SR measures were found, which included private SR practices, beliefs, daily spiritual experiences, three measures of forgiveness, negative religious coping, and purpose in life. Increases in private SR practices and forgiveness of self were the strongest predictors of improvements in drinking outcomes. Changes in daily spiritual experiences, purpose in life, a general measure of forgiveness, and negative religious coping also predicted favorable drinking outcomes. SR change predicted good drinking outcomes in alcoholics, even when controlling for AA involvement. SR variables, broadly defined, deserve attention in fostering change even among those who do not affiliate with AA or religious institutions. Last, future research should include SR variables, particularly various types of forgiveness, given the strong effects found for forgiveness of self.
Is the AIMS65 score useful in predicting outcomes in peptic ulcer bleeding?

Science.gov (United States)

Jung, Sung Hoon; Oh, Jung Hwan; Lee, Hye Yeon; Jeong, Joon Won; Go, Se Eun; You, Chan Ran; Jeon, Eun Jung; Choi, Sang Wook

2014-02-21

To evaluate the applicability of AIMS65 scores in predicting outcomes of peptic ulcer bleeding. This was a retrospective study in a single center between January 2006 and December 2011. We enrolled 522 patients with upper gastrointestinal haemorrhage who visited the emergency room. High-risk patients were regarded as those who had re-bleeding within 30 d from the first endoscopy as well as those who died within 30 d of visiting the Emergency room. A total of 149 patients with peptic ulcer bleeding were analysed, and the AIMS65 score was used to retrospectively predict the high-risk patients. A total of 149 patients with peptic ulcer bleeding were analysed. The poor outcome group comprised 28 patients [male: 23 (82.1%) vs female: 5 (10.7%)] while the good outcome group included 121 patients [male: 93 (76.9%) vs female: 28 (23.1%)]. The mean age in each group was not significantly different. The mean serum albumin levels in the poor outcome group were slightly lower than those in the good outcome group (P = 0.072). For the prediction of poor outcome, the AIMS65 score had a sensitivity of 35.5% (95%CI: 27.0-44.8) and a specificity of 82.1% (95%CI: 63.1-93.9) at a score of 0. The AIMS65 score was insufficient for predicting outcomes in peptic ulcer bleeding (area under curve = 0.571; 95%CI: 0.49-0.65). The AIMS65 score may therefore not be suitable for predicting clinical outcomes in peptic ulcer bleeding. Low albumin levels may be a risk factor associated with high mortality in peptic ulcer bleeding.
Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge.

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Mika Gustafsson

Full Text Available BACKGROUND: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. METHODOLOGY/PRINCIPAL FINDINGS: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the "elastic net". Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. CONCLUSIONS/SIGNIFICANCE: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.
Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads.

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Zarzoso, Vicente; Latcu, Decebal G; Hidalgo-Muñoz, Antonio R; Meo, Marianna; Meste, Olivier; Popescu, Irina; Saoudi, Nadir

2016-12-01

Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only. To assess the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads. Sixty-two patients with persistent AF (52 men; mean age 61.5±10.4years) referred for CA were enrolled. A standard 1-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a non-invasive signal processing algorithm, and combined into a mutivariate prediction model based on logistic regression. During an average follow-up of 13.9±8.3months, 47 patients had no AF recurrence after ablation. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an area under the curve of 0.854, and improving on single-lead amplitude-based predictors. Analysing the f-wave amplitude in several ECG leads simultaneously can significantly improve CA long-term outcome prediction in persistent AF compared with predictors based on single-lead measures. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
Intra-Gene DNA Methylation Variability Is a Clinically Independent Prognostic Marker in Women's Cancers.

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Bartlett, Thomas E; Jones, Allison; Goode, Ellen L; Fridley, Brooke L; Cunningham, Julie M; Berns, Els M J J; Wik, Elisabeth; Salvesen, Helga B; Davidson, Ben; Trope, Claes G; Lambrechts, Sandrina; Vergote, Ignace; Widschwendter, Martin

2015-01-01

We introduce a novel per-gene measure of intra-gene DNA methylation variability (IGV) based on the Illumina Infinium HumanMethylation450 platform, which is prognostic independently of well-known predictors of clinical outcome. Using IGV, we derive a robust gene-panel prognostic signature for ovarian cancer (OC, n = 221), which validates in two independent data sets from Mayo Clinic (n = 198) and TCGA (n = 358), with significance of p = 0.004 in both sets. The OC prognostic signature gene-panel is comprised of four gene groups, which represent distinct biological processes. We show the IGV measurements of these gene groups are most likely a reflection of a mixture of intra-tumour heterogeneity and transcription factor (TF) binding/activity. IGV can be used to predict clinical outcome in patients individually, providing a surrogate read-out of hard-to-measure disease processes.
Analysis and prediction of gene splice sites in four Aspergillus genomes

DEFF Research Database (Denmark)

Wang, Kai; Ussery, David; Brunak, Søren

2009-01-01

Several Aspergillus fungal genomic sequences have been published, with many more in progress. Obviously, it is essential to have high-quality, consistently annotated sets of proteins from each of the genomes, in order to make meaningful comparisons. We have developed a dedicated, publicly available......, splice site prediction program called NetAspGene, for the genus Aspergillus. Gene sequences from Aspergillus fumigatus, the most common mould pathogen, were used to build and test our model. Compared to many animals and plants, Aspergillus contains smaller introns; thus we have applied a larger window...... better splice site prediction than other available tools. NetAspGene will be very helpful for the study in Aspergillus splice sites and especially in alternative splicing. A webpage for NetAspGene is publicly available at http://www.cbs.dtu.dk/services/NetAspGene....
Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

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Glaab, Enrico; Bacardit, Jaume; Garibaldi, Jonathan M; Krasnogor, Natalio

2012-01-01

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.
Mental health predicts better academic outcomes: a longitudinal study of elementary school students in Chile.

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Murphy, J Michael; Guzmán, Javier; McCarthy, Alyssa E; Squicciarini, Ana María; George, Myriam; Canenguez, Katia M; Dunn, Erin C; Baer, Lee; Simonsohn, Ariela; Smoller, Jordan W; Jellinek, Michael S

2015-04-01

The world's largest school-based mental health program, Habilidades para la Vida [Skills for Life (SFL)], has been operating on a national scale in Chile for 15 years. SFL's activities include using standardized measures to screen elementary school students and providing preventive workshops to students at risk for mental health problems. This paper used SFL's data on 37,397 students who were in first grade in 2009 and third grade in 2011 to ascertain whether first grade mental health predicted subsequent academic achievement and whether remission of mental health problems predicted improved academic outcomes. Results showed that mental health was a significant predictor of future academic performance and that, overall, students whose mental health improved between first and third grade made better academic progress than students whose mental health did not improve or worsened. Our findings suggest that school-based mental health programs like SFL may help improve students' academic outcomes.
Predicting radiotherapy outcomes using statistical learning techniques

International Nuclear Information System (INIS)

El Naqa, Issam; Bradley, Jeffrey D; Deasy, Joseph O; Lindsay, Patricia E; Hope, Andrew J

2009-01-01

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model
Fetal omphalocele ratios predict outcomes in prenatally diagnosed omphalocele.

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Montero, Freddy J; Simpson, Lynn L; Brady, Paula C; Miller, Russell S

2011-09-01

The objective of the study was to evaluate whether ratios considering omphalocele diameter relative to fetal biometric measurements perform better than giant omphalocele designation at predicting inability to achieve neonatal primary surgical closure. Cases of fetal omphalocele that underwent evaluation between May 2003 and July 2010 were identified. Inclusion was restricted to live births with plan for postnatal repair. Omphalocele diameter upon antenatal ultrasound was compared with abdominal circumference, femur length, and head circumference, yielding the respective omphalocele (O)/abdominal circumference (AC), O/femur length (FL), and O/head circumference (HC) ratios. The absolute measurements were used to classify giant lesions. Omphalocele ratios and giant omphalocele designations were evaluated as predictors of inability to achieve primary repair. Among 25 included cases, staged or delayed closure occurred in 52%. With an optimal cutoff of 0.21 or greater, O/HC best predicted the primary outcome (sensitivity, 84.6%; specificity, 58.3%; odds ratio, 7.7). The O/HC of 0.21 or greater outperformed giant designations. The O/HC of 0.21 or greater best predicted staged or delayed omphalocele closure. Giant omphalocele designation, regardless of definition, poorly predicted outcome. Copyright © 2011 Mosby, Inc. All rights reserved.
A behavioral economic reward index predicts drinking resolutions: moderation revisited and compared with other outcomes.

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Tucker, Jalie A; Roth, David L; Vignolo, Mary J; Westfall, Andrew O

2009-04-01

Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this "Alcohol-Savings Discretionary Expenditure" index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation.

Bioinformatic prediction and functional characterization of human KIAA0100 gene

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He Cui

2017-02-01

Full Text Available Our previous study demonstrated that human KIAA0100 gene was a novel acute monocytic leukemia-associated antigen (MLAA gene. But the functional characterization of human KIAA0100 gene has remained unknown to date. Here, firstly, bioinformatic prediction of human KIAA0100 gene was carried out using online softwares; Secondly, Human KIAA0100 gene expression was downregulated by the clustered regularly interspaced short palindromic repeats (CRISPR/CRISPR-associated (Cas 9 system in U937 cells. Cell proliferation and apoptosis were next evaluated in KIAA0100-knockdown U937 cells. The bioinformatic prediction showed that human KIAA0100 gene was located on 17q11.2, and human KIAA0100 protein was located in the secretory pathway. Besides, human KIAA0100 protein contained a signalpeptide, a transmembrane region, three types of secondary structures (alpha helix, extended strand, and random coil , and four domains from mitochondrial protein 27 (FMP27. The observation on functional characterization of human KIAA0100 gene revealed that its downregulation inhibited cell proliferation, and promoted cell apoptosis in U937 cells. To summarize, these results suggest human KIAA0100 gene possibly comes within mitochondrial genome; moreover, it is a novel anti-apoptotic factor related to carcinogenesis or progression in acute monocytic leukemia, and may be a potential target for immunotherapy against acute monocytic leukemia.
Predicting Community College Outcomes: Does High School CTE Participation Have a Significant Effect?

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Dietrich, Cecile; Lichtenberger, Eric; Kamalludeen, Rosemaliza

2016-01-01

This study explored the relative importance of participation in high school career and technical education (CTE) programs in predicting community college outcomes. A hierarchical generalized linear model (HGLM) was used to predict community college outcome attainment among a random sample of direct community college entrants. Results show that…
DNA Array-Based Gene Profiling

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Mocellin, Simone; Provenzano, Maurizio; Rossi, Carlo Riccardo; Pilati, Pierluigi; Nitti, Donato; Lise, Mario

2005-01-01

Cancer is a heterogeneous disease in most respects, including its cellularity, different genetic alterations, and diverse clinical behaviors. Traditional molecular analyses are reductionist, assessing only 1 or a few genes at a time, thus working with a biologic model too specific and limited to confront a process whose clinical outcome is likely to be governed by the combined influence of many genes. The potential of functional genomics is enormous, because for each experiment, thousands of relevant observations can be made simultaneously. Accordingly, DNA array, like other high-throughput technologies, might catalyze and ultimately accelerate the development of knowledge in tumor cell biology. Although in its infancy, the implementation of DNA array technology in cancer research has already provided investigators with novel data and intriguing new hypotheses on the molecular cascade leading to carcinogenesis, tumor aggressiveness, and sensitivity to antiblastic agents. Given the revolutionary implications that the use of this technology might have in the clinical management of patients with cancer, principles of DNA array-based tumor gene profiling need to be clearly understood for the data to be correctly interpreted and appreciated. In the present work, we discuss the technical features characterizing this powerful laboratory tool and review the applications so far described in the field of oncology. PMID:15621987
Population effect model identifies gene expression predictors of survival outcomes in lung adenocarcinoma for both Caucasian and Asian patients.

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Guoshuai Cai

Full Text Available We analyzed and integrated transcriptome data from two large studies of lung adenocarcinomas on distinct populations. Our goal was to investigate the variable gene expression alterations between paired tumor-normal tissues and prospectively identify those alterations that can reliably predict lung disease related outcomes across populations.We developed a mixed model that combined the paired tumor-normal RNA-seq from two populations. Alterations in gene expression common to both populations were detected and validated in two independent DNA microarray datasets. A 10-gene prognosis signature was developed through a l1 penalized regression approach and its prognostic value was evaluated in a third independent microarray cohort.Deregulation of apoptosis pathways and increased expression of cell cycle pathways were identified in tumors of both Caucasian and Asian lung adenocarcinoma patients. We demonstrate that a 10-gene biomarker panel can predict prognosis of lung adenocarcinoma in both Caucasians and Asians. Compared to low risk groups, high risk groups showed significantly shorter overall survival time (Caucasian patients data: HR = 3.63, p-value = 0.007; Asian patients data: HR = 3.25, p-value = 0.001.This study uses a statistical framework to detect DEGs between paired tumor and normal tissues that considers variances among patients and ethnicities, which will aid in understanding the common genes and signalling pathways with the largest effect sizes in ethnically diverse cohorts. We propose multifunctional markers for distinguishing tumor from normal tissue and prognosis for both populations studied.
Can Orthopedic Oncologists Predict Functional Outcome in Patients with Sarcoma after Limb Salvage Surgery in the Lower Limb? A Nationwide Study

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Sjoerd Kolk

2014-01-01

Full Text Available Accurate predictions of functional outcome after limb salvage surgery (LSS in the lower limb are important for several reasons, including informing the patient preoperatively and, in some cases, deciding between amputation and LSS. This study aimed to elucidate the correlation between surgeon-predicted and patient-reported functional outcome of LSS in the Netherlands. Twenty-three patients (between six months and ten years after surgery and five independent orthopedic oncologists completed the Toronto Extremity Salvage Score (TESS and the RAND-36 physical functioning subscale (RAND-36 PFS. The orthopedic oncologists made their predictions based on case descriptions (including MRI scans that reflected the preoperative status. The correlation between patient-reported and surgeon-predicted functional outcome was “very poor” to “poor” on both scores (r2 values ranged from 0.014 to 0.354. Patient-reported functional outcome was generally underestimated, by 8.7% on the TESS and 8.3% on the RAND-36 PFS. The most difficult and least difficult tasks on the RAND-36 PFS were also the most difficult and least difficult to predict, respectively. Most questions had a “poor” intersurgeon agreement. It was difficult to accurately predict the patient-reported functional outcome of LSS. Surgeons’ ability to predict functional scores can be improved the most by focusing on accurately predicting more demanding tasks.
GOPET: A tool for automated predictions of Gene Ontology terms

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Glatting Karl-Heinz

2006-03-01

Full Text Available Abstract Background Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO. Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool. It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar Conclusion Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.
Predictors of outcome of group and internet-based cognitive behaviour therapy.

NARCIS (Netherlands)

Spek, V.; Nyklicek, I.; Cuijpers, P.; Pop, V.

2008-01-01

Background: Little is known about which participant characteristics determine the effectiveness of various types of cognitive behavior therapy for sub-threshold depression. The aim of this study was to investigate which characteristics predict treatment outcome of group and internet-based
Comparison of statistical and clinical predictions of functional outcome after ischemic stroke.

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Douglas D Thompson

Full Text Available To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor's clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs.A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS. Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3 using five previously described CPMs. The specificity of a doctor's informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97 and similar to CPMs (range 0.94 to 0.96; however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49 and clinical prediction models (range 0.38 to 0.45 was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76 and CPMs (range 0.69 to 0.75. No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients.CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined.
Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

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Zhili He

2018-02-01

Full Text Available Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN, representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5 increased significantly (P < 0.05 as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.
Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

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Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

2018-02-15

Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.
A meta-analysis based method for prioritizing candidate genes involved in a pre-specific function

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Jingjing Zhai

2016-12-01

Full Text Available The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Towards this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization, in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The ‘leave-one-out’ cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2. Moreover, RAP ranked 53.68% (204/380 flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.
Gene set-based module discovery in the breast cancer transcriptome

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Zhang Michael Q

2009-02-01

Full Text Available Abstract Background Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data. Results In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2 is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells. Conclusion These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.
Intra-Gene DNA Methylation Variability Is a Clinically Independent Prognostic Marker in Women’s Cancers

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Bartlett, Thomas E.; Jones, Allison; Goode, Ellen L.; Fridley, Brooke L.; Cunningham, Julie M.; Berns, Els M. J. J.; Wik, Elisabeth; Salvesen, Helga B.; Davidson, Ben; Trope, Claes G.; Lambrechts, Sandrina; Vergote, Ignace; Widschwendter, Martin

2015-01-01

We introduce a novel per-gene measure of intra-gene DNA methylation variability (IGV) based on the Illumina Infinium HumanMethylation450 platform, which is prognostic independently of well-known predictors of clinical outcome. Using IGV, we derive a robust gene-panel prognostic signature for ovarian cancer (OC, n = 221), which validates in two independent data sets from Mayo Clinic (n = 198) and TCGA (n = 358), with significance of p = 0.004 in both sets. The OC prognostic signature gene-panel is comprised of four gene groups, which represent distinct biological processes. We show the IGV measurements of these gene groups are most likely a reflection of a mixture of intra-tumour heterogeneity and transcription factor (TF) binding/activity. IGV can be used to predict clinical outcome in patients individually, providing a surrogate read-out of hard-to-measure disease processes. PMID:26629914
Density based pruning for identification of differentially expressed genes from microarray data

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Xu Jia

2010-11-01

Full Text Available Abstract Motivation Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune
Review of the quality of total mesorectal excision does not improve the prediction of outcome.

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Demetter, P; Jouret-Mourin, A; Silversmit, G; Vandendael, T; Sempoux, C; Hoorens, A; Nagy, N; Cuvelier, C; Van Damme, N; Penninckx, F

2016-09-01

A fair to moderate concordance in grading of the total mesorectal excision (TME) surgical specimen by local pathologists and a central review panel has been observed in the PROCARE (Project on Cancer of the Rectum) project. The aim of the present study was to evaluate the difference, if any, in the accuracy of predicting the oncological outcome through TME grading by local pathologists or by the review panel. The quality of the TME specimen was reviewed for 482 surgical specimens registered on a prospective database between 2006 and 2011. Patients with a Stage IV tumour, with unknown incidence date or without follow-up information were excluded, resulting in a study population of 383 patients. Quality assessment of the specimen was based on three grades including mesorectal resection (MRR), intramesorectal resection (IMR) and muscularis propria resection (MPR). Using univariable Cox regression models, local and review panel histopathological gradings of the quality of TME were assessed as predictors of local recurrence, distant metastasis and disease-free and overall survival. Differences in the predictions between local and review grading were determined. Resection planes were concordant in 215 (56.1%) specimens. Downgrading from MRR to MPR was noted in 23 (6.0%). There were no significant differences in the prediction error between the two models; local and central review TME grading predicted the outcome equally well. Any difference in grading of the TME specimen between local histopathologists and the review panel had no significant impact on the prediction of oncological outcome for this patient cohort. Grading of the quality of TME as reported by local histopathologists can therefore be used for outcome analysis. Quality control of TME grading is not warranted provided the histopathologist is adequately trained. Colorectal Disease © 2016 The Association of Coloproctology of Great Britain and Ireland.
Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

DEFF Research Database (Denmark)

Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V

2007-01-01

was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...
Black Hole Sign Predicts Poor Outcome in Patients with Intracerebral Hemorrhage.

Science.gov (United States)

Li, Qi; Yang, Wen-Song; Chen, Sheng-Li; Lv, Fu-Rong; Lv, Fa-Jin; Hu, Xi; Zhu, Dan; Cao, Du; Wang, Xing-Chen; Li, Rui; Yuan, Liang; Qin, Xin-Yue; Xie, Peng

2018-01-01

In spontaneous intracerebral hemorrhage (ICH), black hole sign has been proposed as a promising imaging marker that predicts hematoma expansion in patients with ICH. The aim of our study was to investigate whether admission CT black hole sign predicts hematoma growth in patients with ICH. From July 2011 till February 2016, patients with spontaneous ICH who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan were recruited into the study. The presence of black hole sign on admission non-enhanced CT was independently assessed by 2 readers. The functional outcome was assessed using the modified Rankin Scale (mRS) at 90 days. Univariate and multivariable logistic regression analyses were performed to assess the association between the presence of the black hole sign and functional outcome. A total of 225 patients (67.6% male, mean age 60.3 years) were included in our study. Black hole sign was identified in 32 of 225 (14.2%) patients on admission CT scan. The multivariate logistic regression analysis demonstrated that age, intraventricular hemorrhage, baseline ICH volume, admission Glasgow Coma Scale score, and presence of black hole sign on baseline CT independently predict poor functional outcome at 90 days. There are significantly more patients with a poor functional outcome (defined as mRS ≥4) among patients with black hole sign than those without (84.4 vs. 32.1%, p black hole sign independently predicts poor outcome in patients with ICH. Early identification of black hole sign is useful in prognostic stratification and may serve as a potential therapeutic target for anti-expansion clinical trials. © 2018 S. Karger AG, Basel.
Inherited variants in regulatory T cell genes and outcome of ovarian cancer.

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Ellen L Goode

Full Text Available Although ovarian cancer is the most lethal of gynecologic malignancies, wide variation in outcome following conventional therapy continues to exist. The presence of tumor-infiltrating regulatory T cells (Tregs has a role in outcome of this disease, and a growing body of data supports the existence of inherited prognostic factors. However, the role of inherited variants in genes encoding Treg-related immune molecules has not been fully explored. We analyzed expression quantitative trait loci (eQTL and sequence-based tagging single nucleotide polymorphisms (tagSNPs for 54 genes associated with Tregs in 3,662 invasive ovarian cancer cases. With adjustment for known prognostic factors, suggestive results were observed among rarer histological subtypes; poorer survival was associated with minor alleles at SNPs in RGS1 (clear cell, rs10921202, p=2.7×10(-5, LRRC32 and TNFRSF18/TNFRSF4 (mucinous, rs3781699, p=4.5×10(-4, and rs3753348, p=9.0×10(-4, respectively, and CD80 (endometrioid, rs13071247, p=8.0×10(-4. Fo0r the latter, correlative data support a CD80 rs13071247 genotype association with CD80 tumor RNA expression (p=0.006. An additional eQTL SNP in CD80 was associated with shorter survival (rs7804190, p=8.1×10(-4 among all cases combined. As the products of these genes are known to affect induction, trafficking, or immunosuppressive function of Tregs, these results suggest the need for follow-up phenotypic studies.
Inherited variants in regulatory T cell genes and outcome of ovarian cancer.

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Goode, Ellen L; DeRycke, Melissa; Kalli, Kimberly R; Oberg, Ann L; Cunningham, Julie M; Maurer, Matthew J; Fridley, Brooke L; Armasu, Sebastian M; Serie, Daniel J; Ramar, Priya; Goergen, Krista; Vierkant, Robert A; Rider, David N; Sicotte, Hugues; Wang, Chen; Winterhoff, Boris; Phelan, Catherine M; Schildkraut, Joellen M; Weber, Rachel P; Iversen, Ed; Berchuck, Andrew; Sutphen, Rebecca; Birrer, Michael J; Hampras, Shalaka; Preus, Leah; Gayther, Simon A; Ramus, Susan J; Wentzensen, Nicolas; Yang, Hannah P; Garcia-Closas, Montserrat; Song, Honglin; Tyrer, Jonathan; Pharoah, Paul P D; Konecny, Gottfried; Sellers, Thomas A; Ness, Roberta B; Sucheston, Lara E; Odunsi, Kunle; Hartmann, Lynn C; Moysich, Kirsten B; Knutson, Keith L

2013-01-01

Although ovarian cancer is the most lethal of gynecologic malignancies, wide variation in outcome following conventional therapy continues to exist. The presence of tumor-infiltrating regulatory T cells (Tregs) has a role in outcome of this disease, and a growing body of data supports the existence of inherited prognostic factors. However, the role of inherited variants in genes encoding Treg-related immune molecules has not been fully explored. We analyzed expression quantitative trait loci (eQTL) and sequence-based tagging single nucleotide polymorphisms (tagSNPs) for 54 genes associated with Tregs in 3,662 invasive ovarian cancer cases. With adjustment for known prognostic factors, suggestive results were observed among rarer histological subtypes; poorer survival was associated with minor alleles at SNPs in RGS1 (clear cell, rs10921202, p=2.7×10(-5)), LRRC32 and TNFRSF18/TNFRSF4 (mucinous, rs3781699, p=4.5×10(-4), and rs3753348, p=9.0×10(-4), respectively), and CD80 (endometrioid, rs13071247, p=8.0×10(-4)). Fo0r the latter, correlative data support a CD80 rs13071247 genotype association with CD80 tumor RNA expression (p=0.006). An additional eQTL SNP in CD80 was associated with shorter survival (rs7804190, p=8.1×10(-4)) among all cases combined. As the products of these genes are known to affect induction, trafficking, or immunosuppressive function of Tregs, these results suggest the need for follow-up phenotypic studies.
A link prediction method for heterogeneous networks based on BP neural network

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Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

Queue-based modelling and detection of parameters involved in stroke outcome

DEFF Research Database (Denmark)

Vilic, Adnan; Petersen, John Asger; Wienecke, Troels

2017-01-01

We designed a queue-based model, and investigated which parameters are of importance when predicting stroke outcome. Medical record forms have been collected for 57 ischemic stroke patients, including medical history and vital sign measurement along with neurological scores for the first twenty...
Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy.

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Aminsharifi, Alireza; Irani, Dariush; Pooyesh, Shima; Parvin, Hamid; Dehghani, Sakineh; Yousofi, Khalilolah; Fazel, Ebrahim; Zibaie, Fatemeh

2017-05-01

To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable. During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes. Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm 3 . Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%. As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and "learns" the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that
In Silico Analysis of Microarray-Based Gene Expression Profiles Predicts Tumor Cell Response to Withanolides

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Thomas Efferth

2012-05-01

Full Text Available Withania somnifera (L. Dunal (Indian ginseng, winter cherry, Solanaceae is widely used in traditional medicine. Roots are either chewed or used to prepare beverages (aqueous decocts. The major secondary metabolites of Withania somnifera are the withanolides, which are C-28-steroidal lactone triterpenoids. Withania somnifera extracts exert chemopreventive and anticancer activities in vitro and in vivo. The aims of the present in silico study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells towards withanolides. Using IC50 concentrations of eight different withanolides (withaferin A, withaferin A diacetate, 3-azerininylwithaferin A, withafastuosin D diacetate, 4-B-hydroxy-withanolide E, isowithanololide E, withafastuosin E, and withaperuvin and 19 established anticancer drugs, we analyzed the cross-resistance profile of 60 tumor cell lines. The cell lines revealed cross-resistance between the eight withanolides. Consistent cross-resistance between withanolides and nitrosoureas (carmustin, lomustin, and semimustin was also observed. Then, we performed transcriptomic microarray-based COMPARE and hierarchical cluster analyses of mRNA expression to identify mRNA expression profiles predicting sensitivity or resistance towards withanolides. Genes from diverse functional groups were significantly associated with response of tumor cells to withaferin A diacetate, e.g. genes functioning in DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.
Automated prediction of tissue outcome after acute ischemic stroke in computed tomography perfusion images

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Vos, Pieter C.; Bennink, Edwin; de Jong, Hugo; Velthuis, Birgitta K.; Viergever, Max A.; Dankbaar, Jan Willem

2015-03-01

Assessment of the extent of cerebral damage on admission in patients with acute ischemic stroke could play an important role in treatment decision making. Computed tomography perfusion (CTP) imaging can be used to determine the extent of damage. However, clinical application is hindered by differences among vendors and used methodology. As a result, threshold based methods and visual assessment of CTP images has not yet shown to be useful in treatment decision making and predicting clinical outcome. Preliminary results in MR studies have shown the benefit of using supervised classifiers for predicting tissue outcome, but this has not been demonstrated for CTP. We present a novel method for the automatic prediction of tissue outcome by combining multi-parametric CTP images into a tissue outcome probability map. A supervised classification scheme was developed to extract absolute and relative perfusion values from processed CTP images that are summarized by a trained classifier into a likelihood of infarction. Training was performed using follow-up CT scans of 20 acute stroke patients with complete recanalization of the vessel that was occluded on admission. Infarcted regions were annotated by expert neuroradiologists. Multiple classifiers were evaluated in a leave-one-patient-out strategy for their discriminating performance using receiver operating characteristic (ROC) statistics. Results showed that a RandomForest classifier performed optimally with an area under the ROC of 0.90 for discriminating infarct tissue. The obtained results are an improvement over existing thresholding methods and are in line with results found in literature where MR perfusion was used.
Intra-Gene DNA Methylation Variability Is a Clinically Independent Prognostic Marker in Women's Cancers.

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Thomas E Bartlett

Full Text Available We introduce a novel per-gene measure of intra-gene DNA methylation variability (IGV based on the Illumina Infinium HumanMethylation450 platform, which is prognostic independently of well-known predictors of clinical outcome. Using IGV, we derive a robust gene-panel prognostic signature for ovarian cancer (OC, n = 221, which validates in two independent data sets from Mayo Clinic (n = 198 and TCGA (n = 358, with significance of p = 0.004 in both sets. The OC prognostic signature gene-panel is comprised of four gene groups, which represent distinct biological processes. We show the IGV measurements of these gene groups are most likely a reflection of a mixture of intra-tumour heterogeneity and transcription factor (TF binding/activity. IGV can be used to predict clinical outcome in patients individually, providing a surrogate read-out of hard-to-measure disease processes.
Preoperative cow-side lactatemia measurement predicts negative outcome in Holstein dairy cattle with right abomasal disorders.

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Boulay, G; Francoz, D; Doré, E; Dufour, S; Veillette, M; Badillo, M; Bélanger, A-M; Buczinski, S

2014-01-01

The objectives of the current study were (1) to determine the gain in prognostic accuracy of preoperative l-lactate concentration (LAC) measured on farm on cows with right displaced abomasum (RDA) or abomasal volvulus (AV) for predicting negative outcome; and (2) to suggest clinically relevant thresholds for such use. A cohort of 102 cows with on-farm surgical diagnostic of RDA or AV was obtained from June 2009 through December 2011. Blood was drawn from coccygeal vessels before surgery and plasma LAC was immediately measured by using a portable clinical analyzer. Dairy producers were interviewed by phone 30 d following surgery and the outcome was determined: a positive outcome if the owner was satisfied of the overall evolution 30 d postoperatively, and a negative outcome if the cow was culled, died, or if the owner reported being unsatisfied 30 d postoperatively. The area under the curve of the receiver operating characteristic curve for LAC was 0.92 and was significantly greater than the area under the curve of the receiver operating characteristic curve of heart rate (HR; 0.77), indicating that LAC, in general, performed better than HR to predict a negative outcome. Furthermore, the ability to predict a negative outcome was significantly improved when LAC measurement was considered in addition to the already available HR data (area under the curve: 0.93 and 95% confidence interval: 0.87, 0.99). Important inflection points of the misclassification cost term function were noted at thresholds of 2 and 6 mmol/L, suggesting the potential utility of these cut-points. The 2 and 6 mmol/L thresholds had a sensitivity, specificity, positive predictive value, and negative predictive value for predicting a negative outcome of 76.2, 82.7, 53.3, and 93.1%, and of 28.6, 97.5, 75, and 84%, respectively. In terms of clinical interpretation, LAC ≤2 mmol/L appeared to be a good indicator of positive outcome and could be used to support a surgical treatment decision. The
Predictive efficacy of radioisotope voiding cystography for renal outcome

International Nuclear Information System (INIS)

Kim, Seok Ki; Lee, Dong Soo; Kim, Kwang Myeung; Choi, Whang; Chung, June Key; Lee, Myung Chul

2000-01-01

As vesicoureteral reflux (VUR) could lead to renal functional deterioration when combined with urinary tract infection, we need to decide whether operative anti-reflux treatment should be performed at the time of diagnosis of VUR. Predictive value of radioisotope voiding cystography (RIVCG) for renal outcome was tested. In 35 children (18 males, 17 females), radiologic voiding cystoure-thrography (VCU), RIVCG and DMSA scan were performed. Change in renal function was evaluated using the follow-up DMSA scan, ultrasonography, and clinical information. Discriminant analysis was performed using individual or integrated variables such as reflux amount and extent at each phase of voiding on RIVCG, in addition to age, gender and cortical defect on DMSA scan at the time of diagnosis. Discriminant function was composed and its performance was examined. Reflux extent at the filling phase and reflux amount and extent at postvoiding phase had a significant prognostic value. Total reflux amount was a composite variable to predict prognosis. Discriminant function composed of reflux extent at the filling phase and reflux amount and extent at postvoiding phase showed better positive predictive value and specificity than conventional reflux grading. RIVCG could predict renal outcome by disclosing characteristic reflux pattern during various voiding phases.=20
Predicting an optimal outcome after radical prostatectomy: the trifecta nomogram.

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Eastham, James A; Scardino, Peter T; Kattan, Michael W

2008-06-01

The optimal outcome after radical prostatectomy for clinically localized prostate cancer is freedom from biochemical recurrence along with the recovery of continence and erectile function, a so-called trifecta. We evaluated our series of open radical prostatectomy cases to determine the likelihood of this outcome and develop a nomogram predicting the trifecta. We reviewed the records of patients undergoing open radical prostatectomy for clinical stage T1c-T3a prostate cancer at our center during 2000 to 2006. Men were excluded if they received preoperative hormonal therapy, chemotherapy or radiation therapy, if pretreatment prostate specific antigen was more than 50 ng/ml, or if they were impotent or incontinent before radical prostatectomy. A total of 1,577 men were included in the study. Freedom from biochemical recurrence was defined as post-radical prostatectomy prostate specific antigen less than 0.2 ng/ml. Continence was defined as not having to wear any protective pads. Potency was defined as erection adequate for intercourse upon most attempts with or without phosphodiesterase-5 inhibitor. Mean patient age was 58 years and mean pretreatment prostate specific antigen was 6.4 ng/ml. A trifecta outcome (cancer-free status with recovery of continence and potency) was achieved in 62% of patients. In a nomogram developed to predict the likelihood of the trifecta baseline prostate specific antigen was the major predictive factor. Area under the ROC curve for the nomogram was 0.773 and calibration appeared excellent. A trifecta (optimal) outcome can be achieved in most men undergoing radical prostatectomy. The nomogram permits patients to estimate preoperatively their likelihood of an optimal outcome after radical prostatectomy.
Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

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Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.
Predictive modelling of gene expression from transcriptional regulatory elements.

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Budden, David M; Hurley, Daniel G; Crampin, Edmund J

2015-07-01

Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and ϵ-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

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Nidheesh, N; Abdul Nazeer, K A; Ameer, P M

2017-12-01

Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.
Web-based depression treatment : Associations of clients' word use with adherence and outcome

NARCIS (Netherlands)

Van der Zanden, Rianne; Curie, Keshia; van Londen - Barentsen, Monique|info:eu-repo/dai/nl/072312742; Kramer, Jeannet; Steen, Gerard; Cuijpers, Pim

Background: The growing number of web-based psychological treatments, based on textual communication, generates a wealth of data that can contribute to knowledge of online and face-to-face treatments. We investigated whether clients' language use predicted treatment outcomes and adherence in Master
Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Directory of Open Access Journals (Sweden)

Enrico Glaab

Full Text Available Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.
Thermodynamics-based models of transcriptional regulation with gene sequence.

Science.gov (United States)

Wang, Shuqiang; Shen, Yanyan; Hu, Jinxing

2015-12-01

Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

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Surovcik Katharina

2006-03-01

Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired
Cell-specific prediction and application of drug-induced gene expression profiles.

Science.gov (United States)

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.
Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

Directory of Open Access Journals (Sweden)

David M Mutch

Full Text Available BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss could always be differentiated from non-responders (<4 kgs weight loss. We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.
Effect of sample size on multi-parametric prediction of tissue outcome in acute ischemic stroke using a random forest classifier

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Forkert, Nils Daniel; Fiehler, Jens

2015-03-01

The tissue outcome prediction in acute ischemic stroke patients is highly relevant for clinical and research purposes. It has been shown that the combined analysis of diffusion and perfusion MRI datasets using high-level machine learning techniques leads to an improved prediction of final infarction compared to single perfusion parameter thresholding. However, most high-level classifiers require a previous training and, until now, it is ambiguous how many subjects are required for this, which is the focus of this work. 23 MRI datasets of acute stroke patients with known tissue outcome were used in this work. Relative values of diffusion and perfusion parameters as well as the binary tissue outcome were extracted on a voxel-by- voxel level for all patients and used for training of a random forest classifier. The number of patients used for training set definition was iteratively and randomly reduced from using all 22 other patients to only one other patient. Thus, 22 tissue outcome predictions were generated for each patient using the trained random forest classifiers and compared to the known tissue outcome using the Dice coefficient. Overall, a logarithmic relation between the number of patients used for training set definition and tissue outcome prediction accuracy was found. Quantitatively, a mean Dice coefficient of 0.45 was found for the prediction using the training set consisting of the voxel information from only one other patient, which increases to 0.53 if using all other patients (n=22). Based on extrapolation, 50-100 patients appear to be a reasonable tradeoff between tissue outcome prediction accuracy and effort required for data acquisition and preparation.
BLR1 and FCGR1A transcripts in peripheral blood associate with the extent of intrathoracic tuberculosis in children and predict treatment outcome

DEFF Research Database (Denmark)

Jenum, Synne; Bakken, Rasmus; Dhanasekaran, S

2016-01-01

children with intrathoracic tuberculosis (TB), we performed blood transcriptome kinetic analysis during ATT to explore 1) the association between transcriptional biomarkers in whole blood (WB) and the extent of TB disease at diagnosis and treatment outcomes at 2 and 6 months, and 2) the potential...... of the biomarkers to predict treatment response at 2 and 6 months. We present the first data on the association between transcriptional biomarkers and the extent of TB disease as well as outcome of ATT in children: Expression of three genes down-regulated on ATT (FCGR1A, FPR1 and MMP9) exhibited a positive...
Setting the vision: applied patient-reported outcomes and smart, connected digital healthcare systems to improve patient-centered outcomes prediction in critical illness.

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Wysham, Nicholas G; Abernethy, Amy P; Cox, Christopher E

2014-10-01

Prediction models in critical illness are generally limited to short-term mortality and uncommonly include patient-centered outcomes. Current outcome prediction tools are also insensitive to individual context or evolution in healthcare practice, potentially limiting their value over time. Improved prognostication of patient-centered outcomes in critical illness could enhance decision-making quality in the ICU. Patient-reported outcomes have emerged as precise methodological measures of patient-centered variables and have been successfully employed using diverse platforms and technologies, enhancing the value of research in critical illness survivorship and in direct patient care. The learning health system is an emerging ideal characterized by integration of multiple data sources into a smart and interconnected health information technology infrastructure with the goal of rapidly optimizing patient care. We propose a vision of a smart, interconnected learning health system with integrated electronic patient-reported outcomes to optimize patient-centered care, including critical care outcome prediction. A learning health system infrastructure integrating electronic patient-reported outcomes may aid in the management of critical illness-associated conditions and yield tools to improve prognostication of patient-centered outcomes in critical illness.

Long-term outcomes of gene therapy for the treatment of Leber's hereditary optic neuropathy.

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Yang, Shuo; Ma, Si-Qi; Wan, Xing; He, Heng; Pei, Han; Zhao, Min-Jian; Chen, Chen; Wang, Dao-Wen; Dong, Xiao-Yan; Yuan, Jia-Jia; Li, Bin

2016-08-01

Leber's hereditary optic neuropathy (LHON) is a disease that leads to blindness. Gene therapy has been investigated with some success, and could lead to important advancements in treating LHON. This was a prospective, open-label trial involving 9 LHON patients at Tongji Hospital, Wuhan, China, from August 2011 to December 2015. The purpose of this study was to evaluate the long-term outcomes of gene therapy for LHON. Nine LHON patients voluntarily received an intravitreal injection of rAAV2-ND4. Systemic examinations and visual function tests were performed during the 36-month follow-up period to determine the safety and efficacy of this gene therapy. Based on successful experiments in an animal model of LHON, 1 subject also received an rAAV2-ND4 injection in the second eye 12months after gene therapy was administered in the first eye. Recovery of visual acuity was defined as the primary outcome of this study. Changes in the visual field, visual evoked potential (VEP), optical coherence tomography findings, liver and kidney function, and antibodies against AAV2 were defined as secondary endpoints. Eight patients (Patients 2-9) received unilateral gene therapy and visual function improvement was observed in both treated eyes (Patients 4, 6, 7, and 8) and untreated eyes (Patients 2, 3, 4, 6 and 8). Visual regression fluctuations, defined as changes in visual acuity greater than or equal to 0.3 logMAR, were observed in Patients 2 and 9. Age at disease onset, disease duration, and the amount of remaining optic nerve fibers did not have a significant effect on the visual function improvement. The visual field and pattern reversal VEP also improved. The patient (Patient 1) who received gene therapy in both eyes had improved visual acuity in the injected eye after the first treatment. Unfortunately, visual acuity in this eye decreased 3months after he received gene therapy in the second eye. Animal experiments suggested that ND4 expression remains stable in the
Long-term outcomes of gene therapy for the treatment of Leber's hereditary optic neuropathy

Directory of Open Access Journals (Sweden)

Shuo Yang

2016-08-01

Full Text Available Leber's hereditary optic neuropathy (LHON is a disease that leads to blindness. Gene therapy has been investigated with some success, and could lead to important advancements in treating LHON. This was a prospective, open-label trial involving 9 LHON patients at Tongji Hospital, Wuhan, China, from August 2011 to December 2015. The purpose of this study was to evaluate the long-term outcomes of gene therapy for LHON. Nine LHON patients voluntarily received an intravitreal injection of rAAV2-ND4. Systemic examinations and visual function tests were performed during the 36-month follow-up period to determine the safety and efficacy of this gene therapy. Based on successful experiments in an animal model of LHON, 1 subject also received an rAAV2-ND4 injection in the second eye 12 months after gene therapy was administered in the first eye. Recovery of visual acuity was defined as the primary outcome of this study. Changes in the visual field, visual evoked potential (VEP, optical coherence tomography findings, liver and kidney function, and antibodies against AAV2 were defined as secondary endpoints. Eight patients (Patients 2–9 received unilateral gene therapy and visual function improvement was observed in both treated eyes (Patients 4, 6, 7, and 8 and untreated eyes (Patients 2, 3, 4, 6 and 8. Visual regression fluctuations, defined as changes in visual acuity greater than or equal to 0.3 logMAR, were observed in Patients 2 and 9. Age at disease onset, disease duration, and the amount of remaining optic nerve fibers did not have a significant effect on the visual function improvement. The visual field and pattern reversal VEP also improved. The patient (Patient 1 who received gene therapy in both eyes had improved visual acuity in the injected eye after the first treatment. Unfortunately, visual acuity in this eye decreased 3 months after he received gene therapy in the second eye. Animal experiments suggested that ND4 expression remains
Transcriptome dynamics-based operon prediction in prokaryotes.

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Fortino, Vittorio; Smolander, Olli-Pekka; Auvinen, Petri; Tagliaferri, Roberto; Greco, Dario

2014-05-16

Inferring operon maps is crucial to understanding the regulatory networks of prokaryotic genomes. Recently, RNA-seq based transcriptome studies revealed that in many bacterial species the operon structure vary with the change of environmental conditions. Therefore, new computational solutions that use both static and dynamic data are necessary to create condition specific operon predictions. In this work, we propose a novel classification method that integrates RNA-seq based transcriptome profiles with genomic sequence features to accurately identify the operons that are expressed under a measured condition. The classifiers are trained on a small set of confirmed operons and then used to classify the remaining gene pairs of the organism studied. Finally, by linking consecutive gene pairs classified as operons, our computational approach produces condition-dependent operon maps. We evaluated our approach on various RNA-seq expression profiles of the bacteria Haemophilus somni, Porphyromonas gingivalis, Escherichia coli and Salmonella enterica. Our results demonstrate that, using features depending on both transcriptome dynamics and genome sequence characteristics, we can identify operon pairs with high accuracy. Moreover, the combination of DNA sequence and expression data results in more accurate predictions than each one alone. We present a computational strategy for the comprehensive analysis of condition-dependent operon maps in prokaryotes. Our method can be used to generate condition specific operon maps of many bacterial organisms for which high-resolution transcriptome data is available.
Role of Subdural Electrocorticography in Prediction of Long-Term Seizure Outcome in Epilepsy Surgery

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Asano, Eishi; Juhasz, Csaba; Shah, Aashit; Sood, Sandeep; Chugani, Harry T.

2009-01-01

Since prediction of long-term seizure outcome using preoperative diagnostic modalities remains suboptimal in epilepsy surgery, we evaluated whether interictal spike frequency measures obtained from extraoperative subdural electrocorticography (ECoG) recording could predict long-term seizure outcome. This study included 61 young patients (age…
Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

International Nuclear Information System (INIS)

Yu, Jack X; Sieuwerts, Anieta M; Zhang, Yi; Martens, John WM; Smid, Marcel; Klijn, Jan GM; Wang, Yixin; Foekens, John A

2007-01-01

Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different
Dynamic prediction of patient outcomes during ongoing cardiopulmonary resuscitation.

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Kim, Joonghee; Kim, Kyuseok; Callaway, Clifton W; Doh, Kibbeum; Choi, Jungho; Park, Jongdae; Jo, You Hwan; Lee, Jae Hyuk

2017-02-01

The probability of the return of spontaneous circulation (ROSC) and subsequent favourable outcomes changes dynamically during advanced cardiac life support (ACLS). We sought to model these changes using time-to-event analysis in out-of-hospital cardiac arrest (OHCA) patients. Adult (≥18 years old), non-traumatic OHCA patients without prehospital ROSC were included. Utstein variables and initial arterial blood gas measurements were used as predictors. The incidence rate of ROSC during the first 30min of ACLS in the emergency department (ED) was modelled using spline-based parametric survival analysis. Conditional probabilities of subsequent outcomes after ROSC (1-week and 1-month survival and 6-month neurologic recovery) were modelled using multivariable logistic regression. The ROSC and conditional probability models were then combined to estimate the likelihood of achieving ROSC and subsequent outcomes by providing k additional minutes of effort. A total of 727 patients were analyzed. The incidence rate of ROSC increased rapidly until the 10th minute of ED ACLS, and it subsequently decreased. The conditional probabilities of subsequent outcomes after ROSC were also dependent on the duration of resuscitation with odds ratios for 1-week and 1-month survival and neurologic recovery of 0.93 (95% CI: 0.90-0.96, p<0.001), 0.93 (0.88-0.97, p=0.001) and 0.93 (0.87-0.99, p=0.031) per 1-min increase, respectively. Calibration testing of the combined models showed good correlation between mean predicted probability and actual prevalence. The probability of ROSC and favourable subsequent outcomes changed according to a multiphasic pattern over the first 30min of ACLS, and modelling of the dynamic changes was feasible. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

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Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent

2014-02-01

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most
A Behavioral Economic Reward Index Predicts Drinking Resolutions: Moderation Re-visited and Compared with Other Outcomes

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Tucker, Jalie A.; Roth, David L.; Vignolo, Mary J.; Westfall, Andrew O.

2014-01-01

Data were pooled from three studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1-2 year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes compared to abstinence or relapse and to be predicted by more balanced pre-resolution monetary allocations between short- and longer-term objectives (i.e., drinking and saving for the future). Standardized odds ratios (OR) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this “Alcohol-Savings Discretionary Expenditure” index predicted higher rates of both abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of pre-resolution spending patterns aids in predicting moderation. PMID:19309182
Predicting outcome following psychological therapy in IAPT (PROMPT): a naturalistic project protocol.

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Grant, Nina; Hotopf, Matthew; Breen, Gerome; Cleare, Anthony; Grey, Nick; Hepgul, Nilay; King, Sinead; Moran, Paul; Pariante, Carmine M; Wingrove, Janet; Young, Allan H; Tylee, André

2014-06-09

Depression and anxiety are highly prevalent and represent a significant and well described public health burden. Whilst first line psychological treatments are effective for nearly half of attenders, there remain a substantial number of patients who do not benefit. The main objective of the present project is to establish an infrastructure platform for the identification of factors that predict lack of response to psychological treatment for depression and anxiety, in order to better target treatments as well as to support translational and experimental medicine research in mood and anxiety disorders. Predicting outcome following psychological therapy in IAPT (PROMPT) is a naturalistic observational project that began patient recruitment in January 2014. The project is currently taking place in Southwark Psychological Therapies Service, an Improving Access to Psychological Therapies (IAPT) service currently provided by the South London and Maudsley NHS Foundation Trust (SLaM). However, the aim is to roll-out the project across other IAPT services. Participants are approached before beginning treatment and offered a baseline interview whilst they are waiting for therapy to begin. This allows us to test for relationships between predictor variables and patient outcome measures. At the baseline interview, participants complete a diagnostic interview; are asked to give blood and hair samples for relevant biomarkers, and complete psychological and social questionnaire measures. Participants then complete their psychological therapy as offered by Southwark Psychological Therapies Service. Response to psychological therapy will be measured using standard IAPT outcome data, which are routinely collected at each appointment. This project addresses a need to understand treatment response rates in primary care psychological therapy services for those with depression and/or anxiety. Measurement of a range of predictor variables allows for the detection of bio
Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

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Oberthuer, André; Berthold, Frank; Warnat, Patrick; Hero, Barbara; Kahlert, Yvonne; Spitz, Rüdiger; Ernestus, Karen; König, Rainer; Haas, Stefan; Eils, Roland; Schwab, Manfred; Brors, Benedikt; Westermann, Frank; Fischer, Matthias

2006-11-01

To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.
Pre-delivery fibrinogen predicts adverse maternal or neonatal outcomes in patients with placental abruption.

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Wang, Liangcheng; Matsunaga, Shigetaka; Mikami, Yukiko; Takai, Yasushi; Terui, Katsuo; Seki, Hiroyuki

2016-07-01

Placental abruption is a severe obstetric complication of pregnancy that can cause disseminated intravascular coagulation and progress to massive post-partum hemorrhage. Coagulation disorder due to extreme consumption of fibrinogen is considered the main pathogenesis of disseminated intravascular coagulation in patients with placental abruption. The present study sought to determine if the pre-delivery fibrinogen level could predict adverse maternal or neonatal outcomes in patients with placental abruption. This retrospective medical chart review was conducted in a center for maternal, fetal, and neonatal medicine in Japan with 61 patients with placental abruption. Fibrinogen levels prior to delivery were collected and evaluated for the prediction of maternal and neonatal outcomes. The main outcome measures for maternal outcomes were disseminated intravascular coagulation and hemorrhage, and the main outcome measures for neonatal outcomes were Apgar score at 5 min, umbilical artery pH, and stillbirth. The receiver-operator curve and multivariate logistic regression analyses indicated that fibrinogen significantly predicted overt disseminated intravascular coagulation and the requirement of ≥6 red blood cell units, ≥10 fresh frozen plasma units, and ≥20 fresh frozen plasma units for transfusion. Moderate hemorrhage occurred in 71.5% of patients with a decrease in fibrinogen levels to 155 mg/dL. Fibrinogen could also predict neonatal outcomes. Umbilical artery pH neonatal outcomes with placental abruption. © 2016 Japan Society of Obstetrics and Gynecology. © 2016 Japan Society of Obstetrics and Gynecology.
Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach.

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Jing Zhao

Full Text Available Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.
Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups.

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Shiao, S Pamela K; Grayson, James; Yu, Chong Ho; Wasek, Brandi; Bottiglieri, Teodoro

2018-02-16

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene-environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls ( p relation to gene-environment interactions in the prevention of CRC.
Method of predicting Splice Sites based on signal interactions

Directory of Open Access Journals (Sweden)

Deogun Jitender S

2006-04-01

Full Text Available Abstract Background Predicting and proper ranking of canonical splice sites (SSs is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan. Results According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE and Intronic (ISE Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments. Conclusion Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site. Reviewers This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand.
Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

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Assaf Gottlieb

2017-11-01

Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort
Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing

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Vingron Martin

2004-09-01

Full Text Available Abstract Background Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. Methods We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. Results Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. Conclusions We propose to combine the computational prediction of alternative splice isoforms with experimental validation for
Polymorphism at the 3'-UTR of the thymidylate synthase gene: A potential predictor for outcomes in Caucasian patients with esophageal adenocarcinoma treated with preoperative chemoradiation

International Nuclear Information System (INIS)

Liao Zhongxing; Liu Hongji; Swisher, Stephen G.; Wang Luo; Wu, Tsung-Teh; Correa, Arlene M.; Roth, Jack A.; Cox, James D.; Komaki, Ritsuko; Ajani, Jaffer A.; Wei Qingyi

2006-01-01

Purpose: To test the hypothesis that TS3'UTR polymorphisms predict outcomes in 146 Caucasian patients with esophageal adenocarcinoma treated with preoperative 5-fluorouracil-based chemoradiation. Methods and Materials: DNA was extracted from hematoxylin-and-eosin stained histologic slides of normal esophageal or gastric mucosa sections from paraffin blocks of esophagectomy specimens. Genotypes of the TS3'UTR polymorphism were determined by polymerase chain reaction for a 6-bp insertion. The genotype groups (0bp/0bp, 6bp/0bp, and 6bp/6bp) were compared for clinical features and overall survival, recurrence-free-survival, locoregional control (LRC), and distant metastasis control. Multivariable Cox regression analyses were performed to find independent predictors for the stated outcomes. Results: There was a trend of association between 6bp/6bp genotype and a decreased risk of local regional recurrence (hazards ratio = 0.211, 95% confidence interval = 0.041-1.095, p = 0.06) compared with other genotypes. There was a trend that patients with 6bp/6bp genotype had a higher 3-year probability of LRC compared with patients with the other two genotypes combined (p = 0.07); however, the difference was not statistically significant. Conclusions: The null hypotheses were not rejected in this study, probably owing to small sample size or the single gene examined. Prospective studies with adequate statistical power analyzing a family of genes involved in the 5-fluorouracil metabolism are needed to assess genetic determinants of treatment-related outcomes in esophageal adenocarcinoma
Altered expression of HER-2 and the mismatch repair genes MLH1 and MSH2 predicts the outcome of T1 high-grade bladder cancer.

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Sanguedolce, Francesca; Cormio, Antonella; Massenio, Paolo; Pedicillo, Maria C; Cagiano, Simona; Fortunato, Francesca; Calò, Beppe; Di Fino, Giuseppe; Carrieri, Giuseppe; Bufo, Pantaleo; Cormio, Luigi

2018-04-01

The identification of factors predicting the outcome of stage T1 high-grade bladder cancer (BC) is a major clinical issue. We performed immunohistochemistry to assess the role of human epidermal growth factor receptor-2 (HER-2) and microsatellite instability (MSI) factors MutL homologue 1 (MLH1) and MutS homologue 2 (MSH2) in predicting recurrence and progression of T1 high-grade BCs having undergone transurethral resection of bladder tumor (TURBT) alone or TURBT + intravesical instillations of bacillus Calmette-Guerin (BCG). HER-2 overexpression was a significant predictor of disease-free survival (DFS) in the overall as well as in the two patients' population; as for progression-free survival (PFS), it was significant in the overall but not in the two patients' population. MLH1 was an independent predictor of PFS only in patients treated with BCG and MSH2 failed to predict DFS and PFS in all populations. Most importantly, the higher the number of altered markers the lowers the DFS and PFS. In multivariate Cox proportional-hazards regression analysis, the number of altered molecular markers and BCG treatment were significant predictors (p = 0.0004 and 0.0283, respectively) of DFS, whereas the number of altered molecular markers was the only significant predictor (p = 0.0054) of PFS. Altered expression of the proto-oncogene HER-2 and the two molecular markers of genetic instability MLH1 and MSH2 predicted T1 high-grade BC outcome with the higher the number of altered markers the lower the DFS and PFS. These findings provide grounds for further testing them in predicting the outcome of this challenging disease.
Rapid improvements in emotion regulation predict intensive treatment outcome for patients with bulimia nervosa and purging disorder.

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MacDonald, Danielle E; Trottier, Kathryn; Olmsted, Marion P

2017-10-01

Rapid and substantial behavior change (RSBC) early in cognitive behavior therapy (CBT) for eating disorders is the strongest known predictor of treatment outcome. Rapid change in other clinically relevant variables may also be important. This study examined whether rapid change in emotion regulation predicted treatment outcomes, beyond the effects of RSBC. Participants were diagnosed with bulimia nervosa or purging disorder (N = 104) and completed ≥6 weeks of CBT-based intensive treatment. Hierarchical regression models were used to test whether rapid change in emotion regulation variables predicted posttreatment outcomes, defined in three ways: (a) binge/purge abstinence; (b) cognitive eating disorder psychopathology; and (c) depression symptoms. Baseline psychopathology and emotion regulation difficulties and RSBC were controlled for. After controlling for baseline variables and RSBC, rapid improvement in access to emotion regulation strategies made significant unique contributions to the prediction of posttreatment binge/purge abstinence, cognitive psychopathology of eating disorders, and depression symptoms. Individuals with eating disorders who rapidly improve their belief that they can effectively modulate negative emotions are more likely to achieve a variety of good treatment outcomes. This supports the formal inclusion of emotion regulation skills early in CBT, and encouraging patient beliefs that these strategies are helpful. © 2017 Wiley Periodicals, Inc.
Stoichiometric Representation of Gene–Protein–Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

DEFF Research Database (Denmark)

Machado, Daniel; Herrgard, Markus; Rocha, Isabel

2016-01-01

only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene...... design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions...

A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma.

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Jeran K Stratford

2010-07-01

Full Text Available Pancreatic ductal adenocarcinoma (PDAC remains a lethal disease. For patients with localized PDAC, surgery is the best option, but with a median survival of less than 2 years and a difficult and prolonged postoperative course for most, there is an urgent need to better identify patients who have the most aggressive disease.We analyzed the gene expression profiles of primary tumors from patients with localized compared to metastatic disease and identified a six-gene signature associated with metastatic disease. We evaluated the prognostic potential of this signature in a training set of 34 patients with localized and resected PDAC and selected a cut-point associated with outcome using X-tile. We then applied this cut-point to an independent test set of 67 patients with localized and resected PDAC and found that our signature was independently predictive of survival and superior to established clinical prognostic factors such as grade, tumor size, and nodal status, with a hazard ratio of 4.1 (95% confidence interval [CI] 1.7-10.0. Patients defined to be high-risk patients by the six-gene signature had a 1-year survival rate of 55% compared to 91% in the low-risk group.Our six-gene signature may be used to better stage PDAC patients and assist in the difficult treatment decisions of surgery and to select patients whose tumor biology may benefit most from neoadjuvant therapy. The use of this six-gene signature should be investigated in prospective patient cohorts, and if confirmed, in future PDAC clinical trials, its potential as a biomarker should be investigated. Genes in this signature, or the pathways that they fall into, may represent new therapeutic targets. Please see later in the article for the Editors' Summary.
Comparison of the outcome of burn patients using acute-phase plasma base deficit.

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Salehi, S H; As'adi, K; Mousavi, J

2011-12-31

Background. In recent years, plasma base deficit has been used as a marker to determine the status of tissue perfusion in trauma patients and also to predict the outcome of these patients. This study was performed to investigate the effect of plasma base deficit in predicting burn patient outcome. Methods. This prospective cohort study was performed from October 2009 to October 2010 in the acute phase of burn patients who were admitted within 6 h post-injury to Motahari Burn Hospital in Iran. The patients were divided into two groups based on the plasma base deficit in the first 24 h post-injury: group A, in which the mean plasma base deficit was less than or equal to -6 (more negative), and group B, in which the mean plasma base deficit greater than -6. Statistical analysis was performed using SPSS v.16 software. Results. Thirty-eight patients were enrolled in each group. The mean plasma base deficit in group A (-7.76 ± 2.18 mmol) was significantly less than that in group B (-1.19 ± 2.82) mmol (p 0.05) and despite removal of interfering factors, there were significant differences between the systemic inflammatory response syndrome and the multiple organ dysfunction syndrome score and the percentage of sepsis between the two groups (p 0.05). Conclusion. The plasma base deficit can be used as a valuable marker in the resuscitation of burn patients, along with clinical criteria. Physiological indicators (burn percentage, age, and mucosal burns) are not sufficient to predict mortality and morbidity in burn patients, and it is necessary to investigate the role of biochemical markers such as base deficit in determining the final outcome of burn patients.
IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

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Anne-Laure Boulesteix

2017-01-01

Full Text Available As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper, such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.
A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

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Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.
Preoperative MRI findings predict two-year postoperative clinical outcome in lumbar spinal stenosis.

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Pekka Kuittinen

Full Text Available To study the predictive value of preoperative magnetic resonance imaging (MRI findings for the two-year postoperative clinical outcome in lumbar spinal stenosis (LSS.84 patients (mean age 63±11 years, male 43% with symptoms severe enough to indicate LSS surgery were included in this prospective observational single-center study. Preoperative MRI of the lumbar spine was performed with a 1.5-T unit. The imaging protocol conformed to the requirements of the American College of Radiology for the performance of MRI of the adult spine. Visual and quantitative assessment of MRI was performed by one experienced neuroradiologist. At the two-year postoperative follow-up, functional ability was assessed with the Oswestry Disability Index (ODI 0-100% and treadmill test (0-1000 m, pain symptoms with the overall Visual Analogue Scale (VAS 0-100 mm, and specific low back pain (LBP and specific leg pain (LP separately with a numeric rating scale from 0-10 (NRS-11. Satisfaction with the surgical outcome was also assessed.Preoperative severe central stenosis predicted postoperatively lower LP, LBP, and VAS when compared in patients with moderate central stenosis (p<0.05. Moreover, severe stenosis predicted higher postoperative satisfaction (p = 0.029. Preoperative scoliosis predicted an impaired outcome in the ODI (p = 0.031 and lowered the walking distance in the treadmill test (p = 0.001. The preoperative finding of only one stenotic level in visual assessment predicted less postoperative LBP when compared with patients having 2 or more stenotic levels (p = 0.026. No significant differences were detected between quantitative measurements and the patient outcome.Routine preoperative lumbar spine MRI can predict the patient outcome in a two-year follow up in patients with LSS surgery. Severe central stenosis and one-level central stenosis are predictors of good outcome. Preoperative finding of scoliosis may indicate worse functional ability.
Early prediction of outcome of activities of daily living after stroke: a systematic review

OpenAIRE

Veerbeek, J.M.; Kwakkel, G.; Wegen, van, E.E.H.; Ket, J.C.F.; Heijmans, M.W.

2011-01-01

BACKGROUND AND PURPOSE-Knowledge about robust and unbiased factors that predict outcome of activities of daily living (ADL) is paramount in stroke management. This review investigates the methodological quality of prognostic studies in the early poststroke phase for final ADL to identify variables that are predictive or not predictive for outcome of ADL after stroke. METHODS-PubMed, Ebsco/Cinahl and Embase were systematically searched for prognostic studies in which stroke patients were inclu...
Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes

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Xing Li

2014-01-01

Full Text Available Background. Symptoms and signs (symptoms in brief are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM. To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
Genomic biomarkers of prenatal intrauterine inflammation in umbilical cord tissue predict later life neurological outcomes.

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Sloane K Tilley

Full Text Available Preterm birth is a major risk factor for neurodevelopmental delays and disorders. This study aimed to identify genomic biomarkers of intrauterine inflammation in umbilical cord tissue in preterm neonates that predict cognitive impairment at 10 years of age.Genome-wide messenger RNA (mRNA levels from umbilical cord tissue were obtained from 43 neonates born before 28 weeks of gestation. Genes that were differentially expressed across four indicators of intrauterine inflammation were identified and their functions examined. Exact logistic regression was used to test whether expression levels in umbilical cord tissue predicted neurocognitive function at 10 years of age.Placental indicators of inflammation were associated with changes in the mRNA expression of 445 genes in umbilical cord tissue. Transcripts with decreased expression showed significant enrichment for biological signaling processes related to neuronal development and growth. The altered expression of six genes was found to predict neurocognitive impairment when children were 10 years old These genes include two that encode for proteins involved in neuronal development.Prenatal intrauterine inflammation is associated with altered gene expression in umbilical cord tissue. A set of six of the differentially expressed genes predict cognitive impairment later in life, suggesting that the fetal environment is associated with significant adverse effects on neurodevelopment that persist into later childhood.
Motives for sports participation as predictions of self-reported outcomes after anterior cruciate ligament injury of the knee.

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Roessler, K K; Andersen, T E; Lohmander, S; Roos, E M

2015-06-01

Aim of the study was to access how individual's motives for participation in sports impact on self-reported outcomes 2 years after an anterior cruciate ligament injury. Based on a longitudinal cohort study, this secondary analysis present data from the Knee Anterior Cruciate Ligament, Nonsurgical versus Surgical Treatment (KANON) study, a randomized controlled trial. At baseline, 121 patients recorded in an initial questionnaire that their motives for sports participation fell into four categories: achievement, health, social integration, or fun and well-being. These four categories were used as variables in the analyses. All 121 subjects completed the 2-year follow-up. The largest improvement was seen in the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscale sports and recreation function, with an effect size of 2.43. KOOS sports and recreation function was also the subscale score best predicted by the motives for sports participation. Baseline motives achievement and fun and well-being predicted worse levels of pain and function 2 years after the injury, even after adjusting for age, gender, treatment and baseline scores. Psychological aspects, such as motives for participation in sport, can be factors in predicting of patient-reported outcomes 2 years after injury. Evaluating motives for sports participation may help predict the outcome 2 years after ACL injury. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Methodological Challenges in Examining the Impact of Healthcare Predictive Analytics on Nursing-Sensitive Patient Outcomes.

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Jeffery, Alvin D

2015-06-01

The expansion of real-time analytic abilities within current electronic health records has led to innovations in predictive modeling and clinical decision support systems. However, the ability of these systems to influence patient outcomes is currently unknown. Even though nurses are the largest profession within the healthcare workforce, little research has been performed to explore the impact of clinical decision support on their decisions and the patient outcomes associated with them. A scoping literature review explored the impact clinical decision support systems containing healthcare predictive analytics have on four nursing-sensitive patient outcomes (pressure ulcers, failure to rescue, falls, and infections). While many articles discussed variable selection and predictive model development/validation, only four articles examined the impact on patient outcomes. The novelty of predictive analytics and the inherent methodological challenges in studying clinical decision support impact are likely responsible for this paucity of literature. Major methodological challenges include (1) multilevel nature of intervention, (2) treatment fidelity, and (3) adequacy of clinicians' subsequent behavior. There is currently insufficient evidence to demonstrate efficacy of healthcare predictive analytics-enhanced clinical decision support systems on nursing-sensitive patient outcomes. Innovative research methods and a greater emphasis on studying this phenomenon are needed.
Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

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Nikol Snoeren

Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.
An exploratory analysis of criteria for the metabolic syndrome and its prediction of long-term cardiovascular outcomes

NARCIS (Netherlands)

Girman, C.J.; Dekker, J.M.; Rhodes, T.; Nijpels, M.G.A.A.M.; Stehouwer, C.D.A.; Bouter, L.M.; Heine, R.J.

2005-01-01

Studies have shown an increased risk of cardiovascular outcomes with the metabolic syndrome, but information on predictive properties of the National Cholesterol Education Program Adult Treatment Panel 3 (NCEP) criteria is sparse. The authors used data from the Hoorn population-based study in the
Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

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Teng Shaolei

2013-01-01

Full Text Available Abstract Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs and Support Vector Machines (SVMs were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.
Noninvasive work of breathing improves prediction of post-extubation outcome.

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Banner, Michael J; Euliano, Neil R; Martin, A Daniel; Al-Rawas, Nawar; Layon, A Joseph; Gabrielli, Andrea

2012-02-01

We hypothesized that non-invasively determined work of breathing per minute (WOB(N)/min) (esophageal balloon not required) may be useful for predicting extubation outcome, i.e., appropriate work of breathing values may be associated with extubation success, while inappropriately increased values may be associated with failure. Adult candidates for extubation were divided into a training set (n = 38) to determine threshold values of indices for assessing extubation and a prospective validation set (n = 59) to determine the predictive power of the threshold values for patients successfully extubated and those who failed extubation. All were evaluated for extubation during a spontaneous breathing trial (5 cmH(2)O pressure support ventilation, 5 cmH(2)O positive end expiratory pressure) using routine clinical practice standards. WOB(N)/min data were blinded to attending physicians. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive and negative predictive values of all extubation indices were determined. AUC for WOB(N)/min was 0.96 and significantly greater (p indices. WOB(N)/min had a specificity of 0.83, the highest sensitivity at 0.96, positive predictive value at 0.84, and negative predictive value at 0.96 compared to all indices. For 95% of those successfully extubated, WOB(N)/min was ≤10 J/min. WOB(N)/min had the greatest overall predictive accuracy for extubation compared to traditional indices. WOB(N)/min warrants consideration for use in a complementary manner with spontaneous breathing pattern data for predicting extubation outcome.
2009 IOM guidelines for gestational weight gain: how well do they predict outcomes across ethnic groups?

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Khanolkar, Amal R; Hanley, Gillian E; Koupil, Ilona; Janssen, Patricia A

2017-11-13

To determine whether the Institute Of Medicine's (IOM) 2009 guidelines for weight-gain during pregnancy are predictive of maternal and infant outcomes in ethnic minority populations. We designed a population-based study using administrative data on 181,948 women who delivered live singleton births in Washington State between 2006-2008. We examined risks of gestational hypertension, preeclampsia/eclampsia, cesarean delivery, and extended hospital stay in White, Black, Native-American, East-Asian, Hispanic, South-Asian and Hawaiian/Pacific islander women according to whether they gained more or less weight during pregnancy than recommended by IOM guidelines. We also examined risks of neonatal outcomes including Apgar score IOM guidelines for weight gain during pregnancy reduces risk for various adverse maternal outcomes in all ethnic groups studied. However, the guidelines were less predictive of infant outcomes with the exception of small and large for gestational age. GWG: Gestational weight gain; IOM/NRC; Institute of Medicine and National Research Council; NICU: Neonatal intensive care need for ventilation; SGA: Small for gestational age; LGA: Large for gestational age; BERD: Birth Events Records Database; CHARS: Comprehensive Hospital Discharge Abstract Reporting System; ICD: International Classification of Disease; LMP: Last menstrual period; OR: Odds ratio.
An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

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Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives
Prediction modelling for trauma using comorbidity and 'true' 30-day outcome.

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Bouamra, Omar; Jacques, Richard; Edwards, Antoinette; Yates, David W; Lawrence, Thomas; Jenks, Tom; Woodford, Maralyn; Lecky, Fiona

2015-12-01

Prediction models for trauma outcome routinely control for age but there is uncertainty about the need to control for comorbidity and whether the two interact. This paper describes recent revisions to the Trauma Audit and Research Network (TARN) risk adjustment model designed to take account of age and comorbidities. In addition linkage between TARN and the Office of National Statistics (ONS) database allows patient's outcome to be accurately identified up to 30 days after injury. Outcome at discharge within 30 days was previously used. Prospectively collected data between 2010 and 2013 from the TARN database were analysed. The data for modelling consisted of 129 786 hospital trauma admissions. Three models were compared using the area under the receiver operating curve (AuROC) for assessing the ability of the models to predict outcome, the Akaike information criteria to measure the quality between models and test for goodness-of-fit and calibration. Model 1 is the current TARN model, Model 2 is Model 1 augmented by a modified Charlson comorbidity index and Model 3 is Model 2 with ONS data on 30 day outcome. The values of the AuROC curve for Model 1 were 0.896 (95% CI 0.893 to 0.899), for Model 2 were 0.904 (0.900 to 0.907) and for Model 3 0.897 (0.896 to 0.902). No significant interaction was found between age and comorbidity in Model 2 or in Model 3. The new model includes comorbidity and this has improved outcome prediction. There was no interaction between age and comorbidity, suggesting that both independently increase vulnerability to mortality after injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

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Noar, Roslyn D; Daub, Margaret E

2016-01-01

Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity) for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity) to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that they may encode
Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

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Roslyn D Noar

Full Text Available Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that
Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia

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Benedikt Sundermann

2017-06-01

Full Text Available BackgroundThe approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT in PD/AG.MethodsThis analysis is based on pretreatment fMRI data during an interoceptive challenge from a multicenter trial of the German PANIC-NET. Patients with DSM-IV PD/AG were dichotomized as responders (n = 30 or non-responders (n = 29 based on the primary outcome (Hamilton Anxiety Scale Reduction ≥50% after 6 weeks of CBT (2 h/week. fMRI parametric maps were used as features for response classification with linear support vector machines (SVM with or without automated feature selection. Predictive accuracies were assessed using cross validation and permutation testing. The influence of methodological parameters and the predictive ability for specific interoception-related symptom reduction were further evaluated.ResultsSVM did not reach sufficient overall predictive accuracies (38.0–54.2% for anxiety reduction in the primary outcome. In the exploratory analyses, better accuracies (66.7% were achieved for predicting interoception-specific symptom relief as an alternative outcome domain. Subtle information regarding this alternative response criterion but not the primary outcome was revealed by post hoc univariate comparisons.ConclusionIn contrast to reports on other neurofunctional probes, SVM based on an interoception paradigm was not able to reliably predict individual response to CBT. Results speak against the clinical applicability of this technique.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

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Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

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Nolan Priedigkeit

2015-02-01

Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.
Writing Abilities Longitudinally Predict Academic Outcomes of Adolescents with ADHD

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Molitor, Stephen J.; Langberg, Joshuah M.; Bourchtein, Elizaveta; Eddy, Laura D.; Dvorsky, Melissa R.; Evans, Steven W.

2016-01-01

Students with ADHD often experience a host of negative academic outcomes and deficits in reading and mathematics abilities contribute to these academic impairments. Students with ADHD may also have difficulties with written expression but there has been minimal research in this area and it is not clear whether written expression abilities uniquely contribute to the academic functioning of students with ADHD. The current study included a sample of 104 middle school students diagnosed with ADHD (grades 6–8). Participants were followed longitudinally to evaluate whether written expression abilities at baseline predicted student GPA and parent ratings of academic impairment 18 months later, after controlling for reading ability and additional relevant covariates. Written expression abilities longitudinally predicted both academic outcomes above and beyond ADHD and ODD symptoms, medication use, reading ability, and baseline values of GPA and parent-rated academic impairment. Follow-up analyses revealed that no single aspect of written expression was demonstrably more impactful on academic outcomes than the others, suggesting that writing as an entire process should be the focus of intervention. PMID:26783650
Prediction of delayed cerebral ischemia, rebleeding, and outcome after aneurysmal subarachnoid hemorrhage

NARCIS (Netherlands)

Hijdra, A.; van Gijn, J.; Nagelkerke, N. J.; Vermeulen, M.; van Crevel, H.

1988-01-01

Using logistic regression, we analyzed the predictive value of a number of entry variables with respect to the outcome variables delayed cerebral ischemia, rebleeding, and poor outcome (death or severe disability) in patients with aneurysmal subarachnoid hemorrhage. The entry variables were clinical
Cyclophilin B Expression Is Associated with In Vitro Radioresistance and Clinical Outcome after Radiotherapy

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Paul D. Williams

2011-12-01

Full Text Available The tools for predicting clinical outcome after radiotherapy are not yet optimal. To improve on this, we applied the COXEN informatics approach to in vitro radiation sensitivity data of transcriptionally profiled human cells and gene expression data from untreated head and neck squamous cell carcinoma (HNSCC and bladder tumors to generate a multigene predictive model that is independent of histologic findings and reports on tumor radiosensitivity. The predictive ability of this 41-gene model was evaluated in patients with HNSCC and was found to stratify clinical outcome after radiotherapy. In contrast, this model was not useful in stratifying similar patients not treated with radiation. This led us to hypothesize that expression of some of the 41 genes contributes to tumor radioresistance and clinical recurrence. Hence, we evaluated the expression the 41 genes as a function of in vitro radioresistance in the NCI-60 cancer cell line panel and found cyclophilin B (PPIB, a peptidylprolyl isomerase and target of cyclosporine A (CsA, had the strongest direct correlation. Functional inhibition of PPIB by small interfering RNA depletion or CsA treatment leads to radiosensitization in cancer cells and reduced cellular DNA repair. Immunohistochemical evaluation of PPIB expression in patients with HNSCC was found to be associated with outcome after radiotherapy. This work demonstrates that a novel 41-gene expression model of radiation sensitivity developed in bladder cancer cell lines and human skin fibroblasts predicts clinical outcome after radiotherapy in head and neck cancer patients and identifies PPIB as a potential target for clinical radiosensitization.
Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

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Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Watson, David B.; Adams, Michael W. W.; Alm, Eric J.; Adams, Paul D.; Arkin, Adam P.

2018-01-01

ABSTRACT Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. PMID:29463661
Can survival prediction be improved by merging gene expression data sets?

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Haleh Yasrebi

Full Text Available BACKGROUND: High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS: Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS: Merging did not deteriorate performance on average despite (a The diversity of microarray platforms used. (b The heterogeneity of patients cohorts. (c The heterogeneity of breast cancer disease. (d Substantial variation of time to death or relapse. (e The reduced number of genes in the merged data
Relationship between the iceA gene of Helicobacter pylori and clinical outcomes

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Huang XJ

2016-07-01

Full Text Available Xiaojun Huang,1,2 Zhaomin Deng,1 Qiang Zhang,1 Wanyi Li,1 Baoning Wang,1 Mingyuan Li1,3 1Department of Microbiology, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, People’s Republic of China; 2Department of Microbiology, School of Medicine, Hubei University for Nationalities, Enshi, People’s Republic of China; 3State Key Laboratory of Oral Diseases, Chengdu, People’s Republic of China Background: The complex pathogenesis of Helicobacter pylori (H. pylori and the features of the host influence the diverse clinical outcomes. A mass of studies about virulence genes have accelerated the exploration of pathogenesis of H. pylori infection. Induced by contact with epithelium gene A (iceA is one of the biggest concerned virulence genes. In this study, we explored the relationship between iceA and the magnitude of the risk for clinical outcomes and the prevalence of iceA-positive H. pylori in People’s Republic of China and other countries.Methods: We searched the electronic databases of PubMed, Embase, CNKI, VIP, and Wanfang by literature search strategy. The studies conforming to the inclusion criteria were assessed. With these data, we systematically analyzed the relationship between the iceA gene of H. pylori and clinical outcomes.Results: Nineteen articles with 22 studies, a total of 2,657 cases, were involved in the study. The iceA1 gene was significantly associated with peptic ulcer disease (odds ratio =1.28, 95% confidence interval =1.03–1.60; P=0.03, especially in People’s Republic of China (odds ratio =1.40, 95% confidence interval =1.07–1.83; P=0.01. Moreover, the prevalence of iceA1 was significantly higher than iceA2 in People’s Republic of China (P<0.0001. The prevalence of both iceA1 and iceA2 was significantly different (P<0.0001 in People’s Republic of China and in other countries.Conclusion: The system analysis showed that infection with the iceA1-positive H. pylori significantly
Do treatment quality indicators predict cardiovascular outcomes in patients with diabetes?

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Grigory Sidorenkov

Full Text Available BACKGROUND: Landmark clinical trials have led to optimal treatment recommendations for patients with diabetes. Whether optimal treatment is actually delivered in practice is even more important than the efficacy of the drugs tested in trials. To this end, treatment quality indicators have been developed and tested against intermediate outcomes. No studies have tested whether these treatment quality indicators also predict hard patient outcomes. METHODS: A cohort study was conducted using data collected from >10.000 diabetes patients in the Groningen Initiative to Analyze Type 2 Treatment (GIANTT database and Dutch Hospital Data register. Included quality indicators measured glucose-, lipid-, blood pressure- and albuminuria-lowering treatment status and treatment intensification. Hard patient outcome was the composite of cardiovascular events and all-cause death. Associations were tested using Cox regression adjusting for confounding, reporting hazard ratios (HR with 95% confidence intervals. RESULTS: Lipid and albuminuria treatment status, but not blood pressure lowering treatment status, were associated with the composite outcome (HR = 0.77, 0.67-0.88; HR = 0.75, 0.59-0.94. Glucose lowering treatment status was associated with the composite outcome only in patients with an elevated HbA1c level (HR = 0.72, 0.56-0.93. Treatment intensification with glucose-lowering but not with lipid-, blood pressure- and albuminuria-lowering drugs was associated with the outcome (HR = 0.73, 0.60-0.89. CONCLUSION: Treatment quality indicators measuring lipid- and albuminuria-lowering treatment status are valid quality measures, since they predict a lower risk of cardiovascular events and mortality in patients with diabetes. The quality indicators for glucose-lowering treatment should only be used for restricted populations with elevated HbA1c levels. Intriguingly, the tested indicators for blood pressure-lowering treatment did not predict patient
An evolution of trauma care evaluation: A thesis on trauma registry and outcome prediction models

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Joosse, P.

2013-01-01

Outcome prediction models play an invaluable role in the evaluation and improvement of modern trauma care. Trauma registries underlying these outcome prediction models need to be accurate, complete and consistent. This thesis focused on the opportunities and limitations of trauma registries and
How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Energy Technology Data Exchange (ETDEWEB)

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E. (Bette); Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

2016-12-19

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.
Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer

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Giorgio Mustacchi

2013-05-01

Full Text Available Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancer cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections. The biological samples dataset was split into a training (137 cases and a validation set (124 cases. The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes independently associated with DFS: FGF18 (HR = 1.13, p = 0.05, BCL2 (HR = 0.57, p = 0.001, PRC1 (HR = 1.51, p = 0.001, MMP9 (HR = 1.11, p = 0.08, SERF1a (HR = 0.83, p = 0.007. These five genes were combined into a linear score (signature weighted according to the coefficients of the Cox model, as: 0.125FGF18 − 0.560BCL2 + 0.409PRC1 + 0.104MMP9 − 0.188SERF1A (HR = 2.7, 95% CI = 1.9–4.0, p < 0.001. The signature was then evaluated on the validation set assessing the discrimination ability by a Kaplan Meier analysis, using the same cut offs classifying patients at low, intermediate or high risk of disease relapse as defined on the training set (p < 0.001. Our signature, after a further clinical validation, could be proposed as prognostic signature for disease free survival in breast cancer patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain.
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

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Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.
Status epilepticus severity score (STESS): A useful tool to predict outcome of status epilepticus.

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Goyal, Manoj Kumar; Chakravarthi, Sudheer; Modi, Manish; Bhalla, Ashish; Lal, Vivek

2015-12-01

The treatment protocols for status epilepticus (SE) range from small doses of intravenous benzodiazepines to induction of coma. The pros and cons of more aggressive treatment regimen remain debatable. The importance of an index need not be overemphasized which can predict outcome of SE and guide the intensity of treatment. We tried to evaluate utility of one such index Status epilepticus severity score (STESS). 44 consecutive patients of SE were enrolled in the study. STESS results were compared with various outcome measures: (a) mortality, (b) final neurological outcome at discharge as defined by functional independence measure (FIM) (good outcome: FIM score 5-7; bad outcome: FIM score 1-4), (c) control of SE within 1h of start of treatment and (d) need for coma induction. A higher STESS score correlated significantly with poor neurological outcome at discharge (p=0.0001), need for coma induction (p=0.0001) and lack of response to treatment within 1h (p=0.001). A STESS of status epilepticus. Further studies on STESS based treatment approach may help in designing better therapeutic regimens for SE. Copyright © 2015 Elsevier B.V. All rights reserved.
Predicting an Optimal Outcome after Radical Prostatectomy: The “Trifecta” Nomogram

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Eastham, James A.; Scardino, Peter T.; Kattan, Michael W.

2014-01-01

Purpose The optimal outcome after radical prostatectomy (RP) for clinically localized prostate cancer is freedom from biochemical recurrence (BCR) along with recovery of continence and erectile function, a so-called trifecta. We evaluated our series of open radical prostatectomy patients to determine the likelihood of this outcome and to develop a nomogram predicting the trifecta. Material and Methods We reviewed records of patients undergoing open RP for clinical stage T1c–T3a prostate cancer at our center during 2000–2006. Men were excluded if they received preoperative hormonal therapy, chemotherapy, or radiation therapy; if their pre-treatment PSA was >50 ng/ml; or if they were impotent or incontinent before RP; 1577 men were included in the study. Freedom from BCR was defined as post-RP PSA <0.2 ng/ml. Continence was defined as not having to wear any protective pads. Potency was defined as erections adequate for intercourse on the majority of attempts, with or without a phosphodiesterase-5 inhibitor. Results Mean patient age was 58 years and mean pretreatment PSA was 6.4 ng/ml. A trifecta outcome (cancer-free status with recovery of continence and potency) was achieved in 62% of patients. In a nomogram developed to predict the likelihood of the trifecta, baseline PSA was the major predictive factor. The area under the receiver operating characteristic curve for the nomogram was 0.773, and calibration appeared excellent. Conclusions A trifecta (optimal) outcome can be achieved in the majority of men undergoing RP. The nomogram will permit patients to estimate preoperatively their likelihood of an optimal outcome after RP. PMID:18423693
Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

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Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster
Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

International Nuclear Information System (INIS)

Lalonde, Michel; Wassenaar, Richard; Wells, R. Glenn; Birnie, David; Ruddy, Terrence D.

2014-01-01

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster
Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy.

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Hamed Asadi

Full Text Available INTRODUCTION: Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. METHOD: We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. RESULTS: We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408. DISCUSSION: We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter
Post-injury personality in the prediction of outcome following severe acquired brain injury.

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Cattran, Charlotte Jane; Oddy, Michael; Wood, Rodger Llewellyn; Moir, Jane Frances

2011-01-01

The aim of the study was to examine the utility of five measures of non-cognitive neurobehavioural (NCNB) changes that often occur following acquired brain injury, in predicting outcome (measured in terms of participation and social adaptation) at 1-year follow-up. The study employed a longitudinal, correlational design. Multiple regression was employed to investigate the value of five new NCNB measures of social perception, emotional regulation, motivation, impulsivity and disinhibition in the prediction of outcome as measured by the Mayo-Portland Adaptability Inventory (MPAI). Two NCNB measures (motivation and emotional regulation) were found to significantly predict outcome at 1-year follow-up, accounting for 53% of the variance in MPAI total scores. These measures provide a method of quantifying the extent of NCNB changes following brain injury. The predictive value of the measures indicates that they may represent a useful tool which could aid clinicians in identifying early-on those whose symptoms are likely to persist and who may require ongoing intervention. This could facilitate the planning of rehabilitation programmes.
Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

International Nuclear Information System (INIS)

Gevaert, Olivier; De Smet, Frank; Van Gorp, Toon; Pochet, Nathalie; Engelen, Kristof; Amant, Frederic; De Moor, Bart; Timmerman, Dirk; Vergote, Ignace

2008-01-01

In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs. The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models. We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology

Pediatric extracorporeal shock wave lithotripsy: Predicting successful outcomes.

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McAdams, Sean; Shukla, Aseem R

2010-10-01

Extracorporeal shock wave lithotripsy (ESWL) is currently a first-line procedure of most upper urinary tract stones ionizing radiation, perhaps utilizing advancements in ultrasound and magnetic resonance imaging. This report provides a review of the current literature evaluating the patient attributes and stone factors that may be predictive of successful ESWL outcomes along with reviewing the role of pre-operative imaging and considerations for patient safety.
An analysis from the Quality Outcomes Database, Part 1. Disability, quality of life, and pain outcomes following lumbar spine surgery: predicting likely individual patient outcomes for shared decision-making.

Science.gov (United States)

McGirt, Matthew J; Bydon, Mohamad; Archer, Kristin R; Devin, Clinton J; Chotai, Silky; Parker, Scott L; Nian, Hui; Harrell, Frank E; Speroff, Theodore; Dittus, Robert S; Philips, Sharon E; Shaffrey, Christopher I; Foley, Kevin T; Asher, Anthony L

2017-10-01

OBJECTIVE Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifically, clinical registry data are progressively informing health care decision-making. In this analysis, the authors used data from a national prospective outcomes registry (the Quality Outcomes Database) to develop a predictive model for 12-month postoperative pain, disability, and quality of life (QOL) in patients undergoing elective lumbar spine surgery. METHODS Included in this analysis were 7618 patients who had completed 12 months of follow-up. The authors prospectively assessed baseline and 12-month patient-reported outcomes (PROs) via telephone interviews. The PROs assessed were those ascertained using the Oswestry Disability Index (ODI), EQ-5D, and numeric rating scale (NRS) for back pain (BP) and leg pain (LP). Variables analyzed for the predictive model included age, gender, body mass index, race, education level, history of prior surgery, smoking status, comorbid conditions, American Society of Anesthesiologists (ASA) score, symptom duration, indication for surgery, number of levels surgically treated, history of fusion surgery, surgical approach, receipt of workers' compensation, liability insurance, insurance status, and ambulatory ability. To create a predictive model, each 12-month PRO was treated as an ordinal dependent variable and a separate proportional-odds ordinal logistic regression model was fitted for each PRO. RESULTS There was a significant improvement in all PROs (p disability, QOL, and pain outcomes following lumbar spine surgery were employment status, baseline NRS-BP scores, psychological distress, baseline ODI scores, level of education, workers' compensation status, symptom duration, race, baseline NRS-LP scores, ASA score, age, predominant symptom, smoking status, and insurance status. The prediction discrimination of the 4 separate novel predictive models was good, with a c-index of 0.69 for ODI, 0.69 for EQ-5
Integration Strategy Is a Key Step in Network-Based Analysis and Dramatically Affects Network Topological Properties and Inferring Outcomes

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Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu

2014-01-01

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127
Satisfaction of psychotic patients with care and its value to predict outcomes

NARCIS (Netherlands)

Vermeulen, J. M.; Schirmbeck, N. F.; Van Tricht, M. J.; de Haan, L.

2018-01-01

Background: A key indicator of quality of treatment from the patient's perspective is expressed by satisfaction with care. Our aim was to (i) explore satisfaction and its relation to clinical outcome measures; and (ii) explore the predictive value of satisfaction for the course of outcomes over
Predicting Outcome in Patients with Anti-GBM Glomerulonephritis.

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van Daalen, Emma E; Jennette, J Charles; McAdoo, Stephen P; Pusey, Charles D; Alba, Marco A; Poulton, Caroline J; Wolterbeek, Ron; Nguyen, Tri Q; Goldschmeding, Roel; Alchi, Bassam; Griffiths, Meryl; de Zoysa, Janak R; Vincent, Beula; Bruijn, Jan A; Bajema, Ingeborg M

2018-01-06

Large studies on long-term kidney outcome in patients with anti-glomerular basement membrane (anti-GBM) GN are lacking. This study aimed to identify clinical and histopathologic parameters that predict kidney outcome in these patients. This retrospective analysis included a total of 123 patients with anti-GBM GN between 1986 and 2015 from six centers worldwide. Their kidney biopsy samples were classified according to the histopathologic classification for ANCA-associated GN. Clinical data such as details of treatment were retrieved from clinical records. The primary outcome parameter was the occurrence of ESRD. Kidney survival was analyzed using the log-rank test and Cox regression analyses. The 5-year kidney survival rate was 34%, with an improved rate observed among patients diagnosed after 2007 ( P =0.01). In patients with anti-GBM GN, histopathologic class and kidney survival were associated ( P GBM GN. Kidney outcome has improved during recent years; the success rate doubled after 2007. This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2017_11_21_CJASNPodcast_18_1_v.mp3. Copyright © 2018 by the American Society of Nephrology.
Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state.

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Bartek Wilczynski

Full Text Available Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal
Relative size predicts competitive outcome through 2 million years.

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Liow, Lee Hsiang; Di Martino, Emanuela; Krzeminska, Malgorzata; Ramsfjell, Mali; Rust, Seabourne; Taylor, Paul D; Voje, Kjetil L

2017-08-01

Competition is an important biotic interaction that influences survival and reproduction. While competition on ecological timescales has received great attention, little is known about competition on evolutionary timescales. Do competitive abilities change over hundreds of thousands to millions of years? Can we predict competitive outcomes using phenotypic traits? How much do traits that confer competitive advantage and competitive outcomes change? Here we show, using communities of encrusting marine bryozoans spanning more than 2 million years, that size is a significant determinant of overgrowth outcomes: colonies with larger zooids tend to overgrow colonies with smaller zooids. We also detected temporally coordinated changes in average zooid sizes, suggesting that different species responded to a common external driver. Although species-specific average zooid sizes change over evolutionary timescales, species-specific competitive abilities seem relatively stable, suggesting that traits other than zooid size also control overgrowth outcomes and/or that evolutionary constraints are involved. © 2017 John Wiley & Sons Ltd/CNRS.
A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

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2017-02-01

affecting the function of Fanconi Anemia (FA) genes ( FANCA /B/C/D2/E/F/G/I/J/L/M, PALB2) or DNA damage response genes involved in HR 5 (ATM, ATR...Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...To) 15 July 2010 – 2 Nov.2016 4. TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP
Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

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Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

2016-01-01

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079
Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

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Wei Luo

Full Text Available For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD outcomes (four NCDs and two major clinical risk factors, based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88 and those excluded from the development for use as a completely separated validation sample (median correlation 0.85, demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.
Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

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Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

2015-01-01

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.
Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

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Pingzhao Hu

2006-01-01

Full Text Available Background: Microarray technology has been previously used to identify genes that are differentially expressed between tumour and normal samples in a single study, as well as in syntheses involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies summarized probeset-level data, which may potentially lead to a loss of information available at the probe-level. In this paper, we present an approach for integrating results across studies while taking probe-level data into account. Additionally, we follow a new direction in the analysis of microarray expression data, namely to focus on the variation of expression phenotypes in predefined gene sets, such as pathways. This targeted approach can be helpful for revealing information that is not easily visible from the changes in the individual genes. Results: We used a recently developed method to integrate Affymetrix expression data across studies. The idea is based on a probe-level based test statistic developed for testing for differentially expressed genes in individual studies. We incorporated this test statistic into a classic random-effects model for integrating data across studies. Subsequently, we used a gene set enrichment test to evaluate the significance of enriched biological pathways in the differentially expressed genes identified from the integrative analysis. We compared statistical and biological significance of the prognostic gene expression signatures and pathways identified in the probe-level model (PLM with those in the probeset-level model (PSLM. Our integrative analysis of Affymetrix microarray data from 110 prostate cancer samples obtained from three studies reveals thousands of genes significantly correlated with tumour cell differentiation. The bioinformatics analysis, mapping these genes to the publicly available KEGG database, reveals evidence that tumour cell differentiation is significantly associated with many
Predictive Utility of Personality Disorder in Depression: Comparison of Outcomes and Taxonomic Approach.

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Newton-Howes, Giles; Mulder, Roger; Ellis, Pete M; Boden, Joseph M; Joyce, Peter

2017-09-19

There is debate around the best model for diagnosing personality disorder, both in terms of its relationship to the empirical data and clinical utility. Four randomized controlled trials examining various treatments for depression were analyzed at an individual patient level. Three different approaches to the diagnosis of personality disorder were analyzed in these patients. A total of 578 depressed patients were included in the analysis. Personality disorder, however measured, was of little predictive utility in the short term but added significantly to predictive modelling of medium-term outcomes, accounting for more than twice as much of the variance in social functioning outcome as depression psychopathology. Personality disorder assessment is of predictive utility with longer timeframes and when considering social outcomes as opposed to symptom counts. This utility is sufficiently great that there appears to be value in assessing personality; however, no particular approach outperforms any other.
Serotonin Reuptake Inhibitors in Pregnancy: Can Genes Help Us in Predicting Neonatal Adverse Outcome?

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Valentina Giudici

2014-01-01

Full Text Available Lots has been written on use of SSRI during pregnancy and possible short and long term negative outcomes on neonates. the literature so far has described a various field of peripartum illness related to SSRI exposure during foetal life, such as increased incidence of low birth weight, respiratory distress, persistent pulmonary hypertension, poor feeding, and neurobehavioural disease. We know that different degrees of outcomes are possible, and not all the newborns exposed to SSRIs during pregnancy definitely will develop a negative outcome. So far, still little is known about the possible etiologic mechanism that could not only explain the adverse neonatal effects but also the degree of clinical involvement and presentation in the early period after birth. Pharmacogenetics and moreover pharmacogenomics, the study of specific genetic variations and their effect on drug response, are not widespread. This review describes possible relationship between SSRIs pharmacogenetics and different neonatal outcomes and summarizes the current pharmacogenetic inquiries in relation to maternal-foetal environment.
Comparison of Simple Versus Performance-Based Fall Prediction Models

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Shekhar K. Gadkaree BS

2015-05-01

Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.
Endometrial Receptivity and its Predictive Value for IVF/ICSI-Outcome.

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Heger, A; Sator, M; Pietrowski, D

2012-08-01

Endometrial receptivity plays a crucial role in the establishment of a healthy pregnancy in cycles of assisted reproduction. The endometrium as a key factor during reproduction can be assessed in multiple ways, most commonly through transvaginal grey-scale or 3-D ultrasound. It has been shown that controlled ovarian hyperstimulation has a great impact on the uterine lining, which leads to different study results for the predictive value of endometrial factors measured on different cycle days. There is no clear consensus on whether endometrial factors are appropriate to predict treatment outcome and if so, which one is suited best. The aim of this review is to summarize recent findings of studies about the influence of endometrial thickness, volume and pattern on IVF- and ICSI-treatment outcome and provide an overview of future developments in the field.
FDG PET imaging for grading and prediction of outcome in chondrosarcoma patients

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Brenner, Winfried; Eary, Janet F. [Division of Nuclear Medicine, University of Washington Medical Center, 1959 NE Pacific Street, Box 356113, WA 98195-6113, Seattle (United States); Conrad, Ernest U. [Department of Orthopaedics, University of Washington Medical Center, Seattle, WA (United States)

2004-02-01

The aims of this study were to assess the potential of fluorine-18 fluorodeoxyglucose positron emission tomography (FDG PET) for tumor grading in chondrosarcoma patients and to evaluate the role of standardized uptake value (SUV) as a parameter for prediction of patient outcome. FDG PET imaging was performed in 31 patients with chondrosarcoma prior to therapy. SUV was calculated for each tumor and correlated to tumor grade and size, and to patient outcome in terms of local relapse or metastatic disease with a mean follow-up period of 48 months. Chondrosarcomas were detectable in all patients. Tumor SUV was 3.38{+-}1.61 for grade I (n=15), 5.44{+-}3.06 for grade II (n=13), and 7.10{+-}2.61 for grade III (n=3). Significant differences were found between patients with and without disease progression: SUV was 6.42{+-}2.70 (n=10) in patients developing recurrent or metastatic disease compared with 3.74{+-}2.22 in patients without relapse (P=0.015). Using a cut-off of 4 for SUV, sensitivity, specificity, and positive and negative predictive values for a relapse were 90%, 76%, 64%, and 94%, respectively. Combining tumor grade and SUV, these parameters improved to 90%, 95%, 90%, and 95%, respectively. Pretherapeutic tumor SUV obtained by FDG PET imaging was a useful parameter for tumor grading and prediction of outcome in chondrosarcoma patients. The combination of SUV and histopathologic tumor grade further improved prediction of outcome substantially, allowing identification of patients at high risk for local relapse or metastatic disease. (orig.)
Predicting genome-wide redundancy using machine learning

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Shasha Dennis E

2010-11-01

Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.
MMPI-2 Personality Psychopathology Five (PSY-5) and prediction of treatment outcome for patients with chronic back pain

NARCIS (Netherlands)

Vendrig, A.A.; Derksen, J.J.L.; Mey, H.R.A. De

2000-01-01

This study investigated the utility of the MMPI-2-based Personality Psychopathology Five (PSY-5) scales (Harkness, McNulty, & Ben-Porath, 1995) in the outcome prediction of behaviorally oriented chronic-pain treatment. The PSY-5 is a dimensional descriptive system for personality and its disorders.
The Identification of a Threshold of Long Work Hours for Predicting Elevated Risks of Adverse Health Outcomes.

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Conway, Sadie H; Pompeii, Lisa A; Gimeno Ruiz de Porras, David; Follis, Jack L; Roberts, Robert E

2017-07-15

Working long hours has been associated with adverse health outcomes. However, a definition of long work hours relative to adverse health risk has not been established. Repeated measures of work hours among approximately 2,000 participants from the Panel Study of Income Dynamics (1986-2011), conducted in the United States, were retrospectively analyzed to derive statistically optimized cutpoints of long work hours that best predicted three health outcomes. Work-hours cutpoints were assessed for model fit, calibration, and discrimination separately for the outcomes of poor self-reported general health, incident cardiovascular disease, and incident cancer. For each outcome, the work-hours threshold that best predicted increased risk was 52 hours per week or more for a minimum of 10 years. Workers exposed at this level had a higher risk of poor self-reported general health (relative risk (RR) = 1.28; 95% confidence interval (CI): 1.06, 1.53), cardiovascular disease (RR = 1.42; 95% CI: 1.24, 1.63), and cancer (RR = 1.62; 95% CI: 1.22, 2.17) compared with those working 35-51 hours per week for the same duration. This study provides the first health risk-based definition of long work hours. Further examination of the predictive power of this cutpoint on other health outcomes and in other study populations is needed. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Prediction of epigenetically regulated genes in breast cancer cell lines

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Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen; Nautiyal, Shivani; Flaucher, Diane; Carlton, Victoria EH; Moorhead, Martin; Lu, Yontao; Gray, Joe W; Faham, Malek; Spellman, Paul; Parvin, Bahram

2010-05-04

panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.
Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

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Hung, Andrew J; Chen, Jian; Che, Zhengping; Nilanon, Tanachat; Jarc, Anthony; Titus, Micha; Oh, Paul J; Gill, Inderbir S; Liu, Yan

2018-05-01

Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p algorithm in predicting, were largely related to camera manipulation. To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.
A Nonlinear Model for Gene-Based Gene-Environment Interaction

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Jian Sa

2016-06-01

Full Text Available A vast amount of literature has confirmed the role of gene-environment (G×E interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.
An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.

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Zhang, Li; Chen, Jiasheng; Gao, Chunming; Liu, Chuanmiao; Xu, Kuihua

2018-03-16

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.
Prediction of Functional Outcome in Axonal Guillain-Barre Syndrome.

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Sung, Eun Jung; Kim, Dae Yul; Chang, Min Cheol; Ko, Eun Jae

2016-06-01

To identify the factors that could predict the functional outcome in patients with the axonal type of Guillain-Barre syndrome (GBS). Two hundred and two GBS patients admitted to our university hospital between 2003 and 2014 were reviewed retrospectively. We defined a good outcome as being "able to walk independently at 1 month after onset" and a poor outcome as being "unable to walk independently at 1 month after onset". We evaluated the factors that differed between the good and poor outcome groups. Twenty-four patients were classified into the acute motor axonal neuropathy type. There was a statistically significant difference between the good and poor outcome groups in terms of the GBS disability score at admission, and GBS disability score and Medical Research Council sum score at 1 month after admission. In an electrophysiologic analysis, the good outcome group showed greater amplitude of median, ulnar, deep peroneal, and posterior tibial nerve compound muscle action potentials (CMAP) and greater amplitude of median, ulnar, and superficial peroneal sensory nerve action potentials (SNAP) than the poor outcome group. A lower GBS disability score at admission, high amplitude of median, ulnar, deep peroneal, and posterior tibial CMAPs, and high amplitude of median, ulnar, and superficial peroneal SNAPs were associated with being able to walk at 1 month in patients with axonal GBS.
[Collateral score based on CT perfusion can predict the prognosis of patients with anterior circulation ischemic stroke after thrombectomy].

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Wang, Qingsong; Zhang, Sheng; Zhang, Meixia; Chen, Zhicai; Lou, Min

2017-07-25

To evaluate the value of collateral score based on CT perfusion (CTP-CS) in predicting the clinical outcome of patients with anterior circulation ischemic stroke after thrombectomy. Clinical data of acute ischemic stroke patients with anterior artery occlusion undergoing endovascular treatment in the Second Affiliated Hospital, Zhejiang University School of Medicine during October 2013 and October 2016 were retrospectively reviewed. Collateral scores were assessed based on CTP and digital subtraction angiography (DSA) images, respectively. And DSA-CS or CTP-CS 3-4 was defined as good collateral vessels. Good clinical outcome was defined as a modified Rankin Scale (mRS) ≤ 2 at 3 months after stroke. The binary logistic regression model was used to analyze the correlation between the collateral score and clinical outcome, and the receiver operating characteristic (ROC) curve was used to analyze the value of DSA-CS and CTP-CS in predicting the clinical outcome. Among 40 patients, 33 (82.5%) acquired recanalization and 16 (40.0%) got good outcome. Compared with poor outcome group, the collateral score (all P collateral vessels were higher in good outcome group (all P collateral vessels were independent factor of good outcome (CTP-CS: OR =48.404, 95% CI :1.373-1706.585, P Collateral scores based on CTP and DSA had good consistency ( κ =0.697, P <0.01), and ROC curve showed that the predictive value of CTP-CS and DSA-CS were comparable (both AUC=0.726, 95% CI :0.559-0.893, P <0.05). CTP-CS can predict the clinical outcome of patients with anterior circulation ischemic stroke after thrombectomy.
Pediatric extracorporeal shock wave lithotripsy: Predicting successful outcomes

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Sean McAdams

2010-01-01

Full Text Available Extracorporeal shock wave lithotripsy (ESWL is currently a first-line procedure of most upper urinary tract stones <2 cm of size because of established success rates, its minimal invasiveness and long-term safety with minimal complications. Given that alternative surgical and endourological options exist for the management of stone disease and that ESWL failure often results in the need for repeat ESWL or secondary procedures, it is highly desirable to identify variables predicting successful outcomes of ESWL in the pediatric population. Despite numerous reports and growing experience, few prospective studies and guidelines for pediatric ESWL have been completed. Variation in the methods by which study parameters are measured and reported can make it difficult to compare individual studies or make definitive recommendations. There is ongoing work and a need for continuing improvement of imaging protocols in children with renal colic, with a current focus on minimizing exposure to ionizing radiation, perhaps utilizing advancements in ultrasound and magnetic resonance imaging. This report provides a review of the current literature evaluating the patient attributes and stone factors that may be predictive of successful ESWL outcomes along with reviewing the role of pre-operative imaging and considerations for patient safety.
The role of marshall and rotterdam score in predicting 30-day outcome of traumatic brain injury

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Siahaan, A. M. P.; Akbar, T. Y. M.; Nasution, M. D.

2018-03-01

Traumatic brain injury (TBI) remains one of the leading causes of mortality and morbidity, especially in the young population. To predict the outcome of TBI, Marshall, and Rotterdam–CT Scan based scoring was mostly used. As many studies showed conflicting results regarding of the usage of both scoring, this study aims to determine the correlation between Rotterdam and Marshall scoring system with outcome in 30 days and found correlation among them. In 120 subjects with TBI that admitted to Adam Malik General Hospital, we found a significant association of both scorings with the 30-day Glasgow Outcome Score. Therefore, we recommend the use of Marshall and Rotterdam CT Score in initial assessment as a good predictor for patients with TBI.
Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data

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Osamu Komori

2013-01-01

Full Text Available This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.
Leptomeningeal collateral status predicts outcome after middle cerebral artery occlusion

DEFF Research Database (Denmark)

Madelung, Christopher Fugl; Ovesen, C; Trampedach, C

2017-01-01

NCCT and according to European Cooperative Acute Stroke Study (ECASS) criteria. Modified Rankin Scale score was assessed at 90 days, and mortality at 1 year. RESULTS: At 90 days, median (IQR) modified Rankin Scale score in patients with poor collateral status was 4 (3-6) compared to 2 (1-4) in patients...... population (P = .001). CONCLUSIONS: Leptomeningeal collateral status predicts functional outcome, mortality, and hemorrhagic transformation following middle cerebral artery occlusion.......OBJECTIVES: Perfusion through leptomeningeal collateral vessels is a likely pivotal factor in the outcome of stroke patients. We aimed to investigate the effect of collateral status on outcome in a cohort of unselected, consecutive stroke patients with middle cerebral artery occlusion undergoing...
Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy

Directory of Open Access Journals (Sweden)

Chi-Cheng Huang

2013-01-01

Full Text Available Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n=535. The agreement between PAM50 centroid-based single sample prediction (SSP and PLS-regression was excellent (weighted Kappa: 0.988 within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed. Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.
Methylated genes as new cancer biomarkers.

LENUS (Irish Health Repository)

Duffy, M J

2012-02-01

Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested that measurement of the methylation status of the promoter regions of specific genes can aid early detection of cancer, determine prognosis and predict therapy responses. Promising DNA methylation biomarkers include the use of methylated GSTP1 for aiding the early diagnosis of prostate cancer, methylated PITX2 for predicting outcome in lymph node-negative breast cancer patients and methylated MGMT in predicting benefit from alkylating agents in patients with glioblastomas. However, prior to clinical utilisation, these findings require validation in prospective clinical studies. Furthermore, assays for measuring gene methylation need to be standardised, simplified and evaluated in external quality assurance programmes. It is concluded that methylated genes have the potential to provide a new generation of cancer biomarkers.
A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Science.gov (United States)

Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

2013-01-01

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637
Screening Characteristics of TIMI Score in Predicting Acute Coronary Syndrome Outcome; a Diagnostic Accuracy Study

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Mostafa Alavi-Moghaddam

2017-01-01

Full Text Available Introduction: In cases with potential diagnosis of ischemic chest pain, screening high risk patients for adverse outcomes would be very helpful. The present study was designed aiming to determine the diagnostic accuracy of thrombolysis in myocardial infarction (TIMI score in Patients with potential diagnosis of ischemic chest pain.Method: This diagnostic accuracy study was designed to evaluate the screening performance characteristics of TIMI score in predicting 30-day outcomes of mortality, myocardial infarction (MI, and need for revascularization in patients presenting to ED with complaint of typical chest pain and diagnosis of unstable angina or Non-ST elevation MI.Results: 901 patients with the mean age of 58.17 ± 15.00 years (19-90 were studied (52.9% male. Mean TIMI score of the studied patients was 0.97 ± 0.93 (0-5 and the highest frequency of the score belonged to 0 to 2 with 37.2%, 35.3%, and 21.4%, respectively. In total, 170 (18.8% patients experienced the outcomes evaluated in this study. Total sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio of TIMI score were 20 (95% CI: 17 – 24, 99 (95% CI: 97 – 100, 98 (95% CI: 93 – 100, 42 (95% CI: 39 – 46, 58 (95% CI: 14 – 229, and 1.3 (95% CI: 1.2 – 1.4, respectively. Area under the ROC curve of this system for prediction of 30-day mortality, MI, and need for revascularization were 0.51 (95% CI: 0.47 – 0.55, 0.58 (95% CI: 0.54 – 0.62 and 0.56 (95% CI: 0.52 – 0.60, respectively.Conclusion: Based on the findings of the present study, it seems that TIMI score has a high specificity in predicting 30-day adverse outcomes of mortality, MI, and need for revascularization following acute coronary syndrome. However, since its sensitivity, negative predictive value, and negative likelihood ratio are low, it cannot be used as a proper screening tool for ruling out low risk patients in ED.
Tandem repeat variation near the HIC1 (hypermethylated in cancer 1) promoter predicts outcome of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer.

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Okazaki, Satoshi; Schirripa, Marta; Loupakis, Fotios; Cao, Shu; Zhang, Wu; Yang, Dongyun; Ning, Yan; Berger, Martin D; Miyamoto, Yuji; Suenaga, Mitsukuni; Iqubal, Syma; Barzi, Afsaneh; Cremolini, Chiara; Falcone, Alfredo; Battaglin, Francesca; Salvatore, Lisa; Borelli, Beatrice; Helentjaris, Timothy G; Lenz, Heinz-Josef

2017-11-15

The hypermethylated in cancer 1/sirtuin 1 (HIC1/SIRT1) axis plays an important role in regulating the nucleotide excision repair pathway, which is the main oxaliplatin-induced damage-repair system. On the basis of prior evidence that the variable number of tandem repeat (VNTR) sequence located near the promoter lesion of HIC1 is associated with HIC1 gene expression, the authors tested the hypothesis that this VNTR is associated with clinical outcome in patients with metastatic colorectal cancer who receive oxaliplatin-based chemotherapy. Four independent cohorts were tested. Patients who received oxaliplatin-based chemotherapy served as the training cohort (n = 218), and those who received treatment without oxaliplatin served as the control cohort (n = 215). Two cohorts of patients who received oxaliplatin-based chemotherapy were used for validation studies (n = 176 and n = 73). The VNTR sequence near HIC1 was analyzed by polymerase chain reaction analysis and gel electrophoresis and was tested for associations with the response rate, progression-free survival, and overall survival. In the training cohort, patients who harbored at least 5 tandem repeats (TRs) in both alleles had a significantly shorter PFS compared with those who had fewer than 4 TRs in at least 1 allele (9.5 vs 11.6 months; hazard ratio, 1.93; P = .012), and these findings remained statistically significant after multivariate analysis (hazard ratio, 2.00; 95% confidence interval, 1.13-3.54; P = .018). This preliminary association was confirmed in the validation cohort, and patients who had at least 5 TRs in both alleles had a worse PFS compared with the other cohort (7.9 vs 9.8 months; hazard ratio, 1.85; P = .044). The current findings suggest that the VNTR sequence near HIC1 could be a predictive marker for oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer. Cancer 2017;123:4506-14. © 2017 American Cancer Society. © 2017 American Cancer Society.
Prediction of the effect of atrasentan on renal and heart failure outcomes based on short-term changes in multiple risk markers

DEFF Research Database (Denmark)

Schievink, Bauke; de Zeeuw, Dick; Smink, Paul A

2016-01-01

from the RADAR/JAPAN study to predict the effect of atrasentan on renal and heart failure outcomes. METHODS: We performed a post-hoc analysis of the RADAR/JAPAN randomized clinical trials in which 211 patients with type-2 diabetes and nephropathy were randomly assigned to atrasentan 0.75 mg/day, 1......BACKGROUND: A recent phase II clinical trial (Reducing Residual Albuminuria in Subjects with Diabetes and Nephropathy with AtRasentan trial and an identical trial in Japan (RADAR/JAPAN)) showed that the endothelin A receptor antagonist atrasentan lowers albuminuria, blood pressure, cholesterol......, hemoglobin, and increases body weight in patients with type 2 diabetes and nephropathy. We previously developed an algorithm, the Parameter Response Efficacy (PRE) score, which translates short-term drug effects into predictions of long-term effects on clinical outcomes. DESIGN: We used the PRE score on data...
MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

Science.gov (United States)

Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

2017-01-01

Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any
Endosomal gene expression: a new indicator for prostate cancer patient prognosis?

LENUS (Irish Health Repository)

Johnson, Ian R D

2015-11-10

Prostate cancer continues to be a major cause of morbidity and mortality in men, but a method for accurate prognosis in these patients is yet to be developed. The recent discovery of altered endosomal biogenesis in prostate cancer has identified a fundamental change in the cell biology of this cancer, which holds great promise for the identification of novel biomarkers that can predict disease outcomes. Here we have identified significantly altered expression of endosomal genes in prostate cancer compared to non-malignant tissue in mRNA microarrays and confirmed these findings by qRT-PCR on fresh-frozen tissue. Importantly, we identified endosomal gene expression patterns that were predictive of patient outcomes. Two endosomal tri-gene signatures were identified from a previously published microarray cohort and had a significant capacity to stratify patient outcomes. The expression of APPL1, RAB5A, EEA1, PDCD6IP, NOX4 and SORT1 were altered in malignant patient tissue, when compared to indolent and normal prostate tissue. These findings support the initiation of a case-control study using larger cohorts of prostate tissue, with documented patient outcomes, to determine if different combinations of these new biomarkers can accurately predict disease status and clinical progression in prostate cancer patients.
A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

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Albrekt Ann-Sofie

2011-08-01

Full Text Available Abstract Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests.
A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

Science.gov (United States)

2011-01-01

Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests. PMID:21824406

Leptomeningeal collateral status predicts outcome after middle cerebral artery occlusion.

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Madelung, C F; Ovesen, C; Trampedach, C; Christensen, A; Havsteen, I; Hansen, C K; Christensen, H

2018-01-01

Perfusion through leptomeningeal collateral vessels is a likely pivotal factor in the outcome of stroke patients. We aimed to investigate the effect of collateral status on outcome in a cohort of unselected, consecutive stroke patients with middle cerebral artery occlusion undergoing reperfusion therapy. This retrospectively planned analysis was passed on prospectively collected data from 187 consecutive patients with middle cerebral artery occlusion admitted within 4.5 hours to one center and treated with intravenous thrombolysis alone (N = 126), mechanical thrombectomy alone (N = 5), or both (N = 56) from May 2009 to April 2014. Non-contrast CT (NCCT) and computed tomography angiography (CTA) were provided on admission and NCCT repeated at 24 hours. Collateral status was assessed based on the initial CTA. Hemorrhagic transformation was evaluated on the 24-hour NCCT and according to European Cooperative Acute Stroke Study (ECASS) criteria. Modified Rankin Scale score was assessed at 90 days, and mortality at 1 year. At 90 days, median (IQR) modified Rankin Scale score in patients with poor collateral status was 4 (3-6) compared to 2 (1-4) in patients with good collateral status (P collateral status were less likely to achieve a good 90-day outcome (modified Rankin Scale score 0-2) (Adjusted odds ratio 0.27, 95% CI: 0.09-0.86). During the first year, 40.9% of patients with poor collateral status died vs 18.2% of the remaining population (P = .001). Leptomeningeal collateral status predicts functional outcome, mortality, and hemorrhagic transformation following middle cerebral artery occlusion. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

International Nuclear Information System (INIS)

Reis, Patricia P; Simpson, Colleen; Goldstein, David; Brown, Dale; Gilbert, Ralph; Gullane, Patrick; Irish, Jonathan; Jurisica, Igor; Kamel-Reid, Suzanne; Waldron, Levi; Perez-Ordonez, Bayardo; Pintilie, Melania; Galloni, Natalie Naranjo; Xuan, Yali; Cervigne, Nilva K; Warner, Giles C; Makitie, Antti A

2011-01-01

Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence. We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients. We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Over-expression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test). Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

Directory of Open Access Journals (Sweden)

Han Kyungsook

2010-06-01

Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for
Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrroly-sine containing genes

DEFF Research Database (Denmark)

Have, Christian Theil; Zambach, Sine; Christiansen, Henning

2013-01-01

for prediction of pyrrolysine incorporating genes in genomes of bacteria and archaea leading to insights about the factors driving pyrrolysine translation and identification of new gene candidates. The method predicts known conserved genes with high recall and predicts several other promising candidates...... for experimental verification. The method is implemented as a computational pipeline which is available on request....
Leg pain and psychological variables predict outcome 2-3 years after lumbar fusion surgery.

Science.gov (United States)

Abbott, Allan D; Tyni-Lenné, Raija; Hedlund, Rune

2011-10-01

Prediction studies testing a thorough range of psychological variables in addition to demographic, work-related and clinical variables are lacking in lumbar fusion surgery research. This prospective cohort study aimed at examining predictions of functional disability, back pain and health-related quality of life (HRQOL) 2-3 years after lumbar fusion by regressing nonlinear relations in a multivariate predictive model of pre-surgical variables. Before and 2-3 years after lumbar fusion surgery, patients completed measures investigating demographics, work-related variables, clinical variables, functional self-efficacy, outcome expectancy, fear of movement/(re)injury, mental health and pain coping. Categorical regression with optimal scaling transformation, elastic net regularization and bootstrapping were used to investigate predictor variables and address predictive model validity. The most parsimonious and stable subset of pre-surgical predictor variables explained 41.6, 36.0 and 25.6% of the variance in functional disability, back pain intensity and HRQOL 2-3 years after lumbar fusion. Pre-surgical control over pain significantly predicted functional disability and HRQOL. Pre-surgical catastrophizing and leg pain intensity significantly predicted functional disability and back pain while the pre-surgical straight leg raise significantly predicted back pain. Post-operative psychomotor therapy also significantly predicted functional disability while pre-surgical outcome expectations significantly predicted HRQOL. For the median dichotomised classification of functional disability, back pain intensity and HRQOL levels 2-3 years post-surgery, the discriminative ability of the prediction models was of good quality. The results demonstrate the importance of pre-surgical psychological factors, leg pain intensity, straight leg raise and post-operative psychomotor therapy in the predictions of functional disability, back pain and HRQOL-related outcomes.
A Shortest-Path-Based Method for the Analysis and Prediction of Fruit-Related Genes in Arabidopsis thaliana.

Science.gov (United States)

Zhu, Liucun; Zhang, Yu-Hang; Su, Fangchu; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2016-01-01

Biologically, fruits are defined as seed-bearing reproductive structures in angiosperms that develop from the ovary. The fertilization, development and maturation of fruits are crucial for plant reproduction and are precisely regulated by intrinsic genetic regulatory factors. In this study, we used Arabidopsis thaliana as a model organism and attempted to identify novel genes related to fruit-associated biological processes. Specifically, using validated genes, we applied a shortest-path-based method to identify several novel genes in a large network constructed using the protein-protein interactions observed in Arabidopsis thaliana. The described analyses indicate that several of the discovered genes are associated with fruit fertilization, development and maturation in Arabidopsis thaliana.
Prediction of methylphenidate treatment outcome in adults with attention-deficit/hyperactivity disorder (ADHD).

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Retz, Wolfgang; Retz-Junginger, Petra

2014-11-01

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often persists in adulthood. Methylphenidate (MPH) is one of the most effective medications to treat ADHD, but also few adult patients show no sufficient response to this drug. In this paper, we give an overview regarding genetic, neuroimaging, clinical and other studies which have tried to reveal the reasons for non-response in adults with ADHD, based on a systematic literature search. Although MPH is a well-established treatment for adults with ADHD, research regarding the prediction of treatment outcome is still limited and has resulted in inconsistent findings. No reliable neurobiological markers of treatment response have been identified so far. Some findings from clinical studies suggest that comorbidity with substance use disorders and personality disorders has an impact on treatment course and outcome. As MPH is widely used in the treatment of adults with ADHD, much more work is needed regarding positive and negative predictors of long-term treatment outcome in order to optimize the pharmacological treatment of adult ADHD patients.
Presurgery resting-state local graph-theory measures predict neurocognitive outcomes after brain surgery in temporal lobe epilepsy.

Science.gov (United States)

Doucet, Gaelle E; Rider, Robert; Taylor, Nathan; Skidmore, Christopher; Sharan, Ashwini; Sperling, Michael; Tracy, Joseph I

2015-04-01

This study determined the ability of resting-state functional connectivity (rsFC) graph-theory measures to predict neurocognitive status postsurgery in patients with temporal lobe epilepsy (TLE) who underwent anterior temporal lobectomy (ATL). A presurgical resting-state functional magnetic resonance imaging (fMRI) condition was collected in 16 left and 16 right TLE patients who underwent ATL. In addition, patients received neuropsychological testing pre- and postsurgery in verbal and nonverbal episodic memory, language, working memory, and attention domains. Regarding the functional data, we investigated three graph-theory properties (local efficiency, distance, and participation), measuring segregation, integration and centrality, respectively. These measures were only computed in regions of functional relevance to the ictal pathology, or the cognitive domain. Linear regression analyses were computed to predict the change in each neurocognitive domain. Our analyses revealed that cognitive outcome was successfully predicted with at least 68% of the variance explained in each model, for both TLE groups. The only model not significantly predictive involved nonverbal episodic memory outcome in right TLE. Measures involving the healthy hippocampus were the most common among the predictors, suggesting that enhanced integration of this structure with the rest of the brain may improve cognitive outcomes. Regardless of TLE group, left inferior frontal regions were the best predictors of language outcome. Working memory outcome was predicted mostly by right-sided regions, in both groups. Overall, the results indicated our integration measure was the most predictive of neurocognitive outcome. In contrast, our segregation measure was the least predictive. This study provides evidence that presurgery rsFC measures may help determine neurocognitive outcomes following ATL. The results have implications for refining our understanding of compensatory reorganization and predicting
Poor outcome prediction by burst suppression ratio in adults with post-anoxic coma without hypothermia.

Science.gov (United States)

Yang, Qinglin; Su, Yingying; Hussain, Mohammed; Chen, Weibi; Ye, Hong; Gao, Daiquan; Tian, Fei

2014-05-01

Burst suppression ratio (BSR) is a quantitative electroencephalography (qEEG) parameter. The purpose of our study was to compare the accuracy of BSR when compared to other EEG parameters in predicting poor outcomes in adults who sustained post-anoxic coma while not being subjected to therapeutic hypothermia. EEG was registered and recorded at least once within 7 days of post-anoxic coma onset. Electrodes were placed according to the international 10-20 system, using a 16-channel layout. Each EEG expert scored raw EEG using a grading scale adapted from Young and scored amplitude-integrated electroencephalography tracings, in addition to obtaining qEEG parameters defined as BSR with a defined threshold. Glasgow outcome scales of 1 and 2 at 3 months, determined by two blinded neurologists, were defined as poor outcome. Sixty patients with Glasgow coma scale score of 8 or less after anoxic accident were included. The sensitivity (97.1%), specificity (73.3%), positive predictive value (82.5%), and negative prediction value (95.0%) of BSR in predicting poor outcome were higher than other EEG variables. BSR1 and BSR2 were reliable in predicting death (area under the curve > 0.8, P coma who do not undergo therapeutic hypothermia when compared to other qEEG parameters.
Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms

NARCIS (Netherlands)

Drukker, C.A.; Nijenhuis, M.V.; Bueno de Mesquita, J.M.; Retel, V.P.; Retel, Valesca; van Harten, Willem H.; van Tinteren, H.; Wesseling, J.; Schmidt, M.K.; van 't Veer, L.J.; Sonke, G.S.; Rutgers, E.J.T.; van de Vijver, M.J.; Linn, S.C.

2014-01-01

Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether
Data-Based Predictive Control with Multirate Prediction Step

Science.gov (United States)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.
HOX Gene Promoter Prediction and Inter-genomic Comparison: An Evo-Devo Study

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Marla A. Endriga

2010-10-01

Full Text Available Homeobox genes direct the anterior-posterior axis of the body plan in eukaryotic organisms. Promoter regions upstream of the Hox genes jumpstart the transcription process. CpG islands found within the promoter regions can cause silencing of these promoters. The locations of the promoter regions and the CpG islands of Homeo sapiens sapiens (human, Pan troglodytes (chimpanzee, Mus musculus (mouse, and Rattus norvegicus (brown rat are compared and related to the possible influence on the specification of the mammalian body plan. The sequence of each gene in Hox clusters A-D of the mammals considered were retrieved from Ensembl and locations of promoter regions and CpG islands predicted using Exon Finder. The predicted promoter sequences were confirmed via BLAST and verified against the Eukaryotic Promoter Database. The significance of the locations was determined using the Kruskal-Wallis test. Among the four clusters, only promoter locations in cluster B showed significant difference. HOX B genes have been linked with the control of genes that direct the development of axial morphology, particularly of the vertebral column bones. The magnitude of variation among the body plans of closely-related species can thus be partially attributed to the promoter kind, location and number, and gene inactivation via CpG methylation.
Predicting Outcome in Comatose Patients: The Role of EEG Reactivity to Quantifiable Electrical Stimuli

Directory of Open Access Journals (Sweden)

Gang Liu

2016-01-01

Full Text Available Objective. To test the value of quantifiable electrical stimuli as a reliable method to assess electroencephalogram reactivity (EEG-R for the early prognostication of outcome in comatose patients. Methods. EEG was recorded in consecutive adults in coma after cardiopulmonary resuscitation (CPR or stroke. EEG-R to standard electrical stimuli was tested. Each patient received a 3-month follow-up by the Glasgow-Pittsburgh cerebral performance categories (CPC or modified Rankin scale (mRS score. Results. Twenty-two patients met the inclusion criteria. In the CPR group, 6 of 7 patients with EEG-R had good outcomes (positive predictive value (PPV, 85.7% and 4 of 5 patients without EEG-R had poor outcomes (negative predictive value (NPV, 80%. The sensitivity and specificity were 85.7% and 80%, respectively. In the stroke group, 6 of 7 patients with EEG-R had good outcomes (PPV, 85.7%; all of the 3 patients without EEG-R had poor outcomes (NPV, 100%. The sensitivity and specificity were 100% and 75%, respectively. Of all patients, the presence of EEG-R showed 92.3% sensitivity, 77.7% specificity, 85.7% PPV, and 87.5% NPV. Conclusion. EEG-R to quantifiable electrical stimuli might be a good positive predictive factor for the prognosis of outcome in comatose patients after CPR or stroke.
Introduction: Cancer Gene Networks.

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Clarke, Robert

2017-01-01

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary
Executive function processes predict mobility outcomes in older adults.

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Gothe, Neha P; Fanning, Jason; Awick, Elizabeth; Chung, David; Wójcicki, Thomas R; Olson, Erin A; Mullen, Sean P; Voss, Michelle; Erickson, Kirk I; Kramer, Arthur F; McAuley, Edward

2014-02-01

To examine the relationship between performance on executive function measures and subsequent mobility outcomes in community-dwelling older adults. Randomized controlled clinical trial. Champaign-Urbana, Illinois. Community-dwelling older adults (N = 179; mean age 66.4). A 12-month exercise trial with two arms: an aerobic exercise group and a stretching and strengthening group. Established cognitive tests of executive function (flanker task, task switching, and a dual-task paradigm) and the Wisconsin card sort test. Mobility was assessed using the timed 8-foot up and go test and times to climb up and down a flight of stairs. Participants completed the cognitive tests at baseline and the mobility measures at baseline and after 12 months of the intervention. Multiple regression analyses were conducted to determine whether baseline executive function predicted postintervention functional performance after controlling for age, sex, education, cardiorespiratory fitness, and baseline mobility levels. Selective baseline executive function measurements, particularly performance on the flanker task (β = 0.15-0.17) and the Wisconsin card sort test (β = 0.11-0.16) consistently predicted mobility outcomes at 12 months. The estimates were in the expected direction, such that better baseline performance on the executive function measures predicted better performance on the timed mobility tests independent of intervention. Executive functions of inhibitory control, mental set shifting, and attentional flexibility were predictive of functional mobility. Given the literature associating mobility limitations with disability, morbidity, and mortality, these results are important for understanding the antecedents to poor mobility function that well-designed interventions to improve cognitive performance can attenuate. © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society.
Prediction of extubation outcome in preterm infants by composite extubation indices.

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Dimitriou, Gabriel; Fouzas, Sotirios; Vervenioti, Aggeliki; Tzifas, Sotirios; Mantagos, Stefanos

2011-11-01

To determine whether composite extubation indices can predict extubation outcome in preterm infants. Prospective observational study. Level III neonatal intensive care unit. Fifty-six preterm infants cared for in the neonatal intensive care unit of a tertiary teaching hospital during 2007 and 2008. None. The study consisted of two parts. In the first part, different extubation indices were evaluated in a group of 28 neonates (derivation group). These indices included the diaphragmatic pressure-time index, the respiratory muscle pressure-time index, the maximal transdiaphragmatic pressure, the maximal inspiratory pressure, the airway pressure generated 100 milliseconds after an occlusion/maximal transdiaphragmatic pressure ratio, the airway pressure generated 100 milliseconds after an occlusion/maximal inspiratory pressure ratio, the tidal volume, and the respiratory rate to tidal volume ratio. After exploratory analysis, the best performing indices and the optimal threshold values to predict extubation outcome were selected. In the second part of the study, these indices were validated at the predetermined threshold values in an additional group of 28 preterm neonates (validation group). Four infants (14.3%) in the derivation group and four in the validation group (14.3%) failed extubation. Receiver operator characteristic curve analysis revealed that a diaphragmatic pressure-time index of ≤0.12, a respiratory muscle pressure-time index ≤0.10, a airway pressure generated 100 milliseconds after an occlusion/maximal transdiaphragmatic pressure of ≤0.14, and a airway pressure generated 100 milliseconds after an occlusion/maximal inspiratory pressure of ≤0.09 were the most accurate predictors of extubation outcome in the derivation group. In the validation group, a diaphragmatic pressure-time index of ≤0.12 and a respiratory muscle pressure-time index of ≤0.10 both had zero false-positive results, predicting with accuracy successful extubation. Composite
Breast-cancer-specific mortality in patients treated based on the 21-gene assay: a SEER population-based study.

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Petkov, Valentina I; Miller, Dave P; Howlader, Nadia; Gliner, Nathan; Howe, Will; Schussler, Nicola; Cronin, Kathleen; Baehner, Frederick L; Cress, Rosemary; Deapen, Dennis; Glaser, Sally L; Hernandez, Brenda Y; Lynch, Charles F; Mueller, Lloyd; Schwartz, Ann G; Schwartz, Stephen M; Stroup, Antoinette; Sweeney, Carol; Tucker, Thomas C; Ward, Kevin C; Wiggins, Charles; Wu, Xiao-Cheng; Penberthy, Lynne; Shak, Steven

2016-01-01

The 21-gene Recurrence Score assay is validated to predict recurrence risk and chemotherapy benefit in hormone-receptor-positive (HR+) invasive breast cancer. To determine prospective breast-cancer-specific mortality (BCSM) outcomes by baseline Recurrence Score results and clinical covariates, the National Cancer Institute collaborated with Genomic Health and 14 population-based registries in the the Surveillance, Epidemiology, and End Results (SEER) Program to electronically supplement cancer surveillance data with Recurrence Score results. The prespecified primary analysis cohort was 40-84 years of age, and had node-negative, HR+, HER2-negative, nonmetastatic disease diagnosed between January 2004 and December 2011 in the entire SEER population, and Recurrence Score results ( N =38,568). Unadjusted 5-year BCSM were 0.4% ( n =21,023; 95% confidence interval (CI), 0.3-0.6%), 1.4% ( n =14,494; 95% CI, 1.1-1.7%), and 4.4% ( n =3,051; 95% CI, 3.4-5.6%) for Recurrence Score <18, 18-30, and ⩾31 groups, respectively ( P <0.001). In multivariable analysis adjusted for age, tumor size, grade, and race, the Recurrence Score result predicted BCSM ( P <0.001). Among patients with node-positive disease (micrometastases and up to three positive nodes; N =4,691), 5-year BCSM (unadjusted) was 1.0% ( n =2,694; 95% CI, 0.5-2.0%), 2.3% ( n =1,669; 95% CI, 1.3-4.1%), and 14.3% ( n =328; 95% CI, 8.4-23.8%) for Recurrence Score <18, 18-30, ⩾31 groups, respectively ( P <0.001). Five-year BCSM by Recurrence Score group are reported for important patient subgroups, including age, race, tumor size, grade, and socioeconomic status. This SEER study represents the largest report of prospective BCSM outcomes based on Recurrence Score results for patients with HR+, HER2-negative, node-negative, or node-positive breast cancer, including subgroups often under-represented in clinical trials.
Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

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Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

2013-10-01

Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Genomic Prediction of Gene Bank Wheat Landraces

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José Crossa

2016-07-01

Full Text Available This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H for the highly heritable traits, days to heading (DTH, and days to maturity (DTM. Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E. Two alternative prediction strategies were studied: (1 random cross-validation of the data in 20% training (TRN and 80% testing (TST (TRN20-TST80 sets, and (2 two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm
Prediction of the contact sensitizing potential of chemicals using analysis of gene expression changes in human THP-1 monocytes.

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Arkusz, Joanna; Stępnik, Maciej; Sobala, Wojciech; Dastych, Jarosław

2010-11-10

The aim of this study was to find differentially regulated genes in THP-1 monocytic cells exposed to sensitizers and nonsensitizers and to investigate if such genes could be reliable markers for an in vitro predictive method for the identification of skin sensitizing chemicals. Changes in expression of 35 genes in the THP-1 cell line following treatment with chemicals of different sensitizing potential (from nonsensitizers to extreme sensitizers) were assessed using real-time PCR. Verification of 13 candidate genes by testing a large number of chemicals (an additional 22 sensitizers and 8 nonsensitizers) revealed that prediction of contact sensitization potential was possible based on evaluation of changes in three genes: IL8, HMOX1 and PAIMP1. In total, changes in expression of these genes allowed correct detection of sensitization potential of 21 out of 27 (78%) test sensitizers. The gene expression levels inside potency groups varied and did not allow estimation of sensitization potency of test chemicals. Results of this study indicate that evaluation of changes in expression of proposed biomarkers in THP-1 cells could be a valuable model for preliminary screening of chemicals to discriminate an appreciable majority of sensitizers from nonsensitizers. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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