WorldWideScience

Sample records for gene prediction artifacts

  1. Artifacts

    Science.gov (United States)

    Harris, Samantha

    2009-01-01

    NASA Headquarters sent a list of items to KSC that were deemed potential artifacts. These items played arole in the Shuttle Program's development and maintenance. Because these items are national assets, many are of interest to museums, schools, other government entities, etc. upon the Space Shuttle's retirement. The list contains over 500 items. All of these items need to be located, photographed, and catalogued with accompanying specific data that needs to be gathered. Initial research suggests that this is a time, labor, and cost intensive project. The purpose of my project was to focus on 20-60 of these 500 items, gather the necessary data, and compile them in a way that can be added to by other users when/if the project goes into full effect.

  2. Archaeology Through Computational Linguistics: Inscription Statistics Predict Excavation Sites of Indus Valley Artifacts.

    Science.gov (United States)

    Recchia, Gabriel L; Louwerse, Max M

    2016-11-01

    Computational techniques comparing co-occurrences of city names in texts allow the relative longitudes and latitudes of cities to be estimated algorithmically. However, these techniques have not been applied to estimate the provenance of artifacts with unknown origins. Here, we estimate the geographic origin of artifacts from the Indus Valley Civilization, applying methods commonly used in cognitive science to the Indus script. We show that these methods can accurately predict the relative locations of archeological sites on the basis of artifacts of known provenance, and we further apply these techniques to determine the most probable excavation sites of four sealings of unknown provenance. These findings suggest that inscription statistics reflect historical interactions among locations in the Indus Valley region, and they illustrate how computational methods can help localize inscribed archeological artifacts of unknown origin. The success of this method offers opportunities for the cognitive sciences in general and for computational anthropology specifically.

  3. MRI Artifacts

    Directory of Open Access Journals (Sweden)

    Abed Al Nasser Assi

    2009-12-01

    Full Text Available   "nMagnetic resonance imaging (MRI has become more and more frequently used in medical imaging diagnostic in recent years. Radiologists and technicians working at these systems are relatively often confronted with image artifacts related to the radiowave with strong magnetic in the scanner. Many artifacts may be corrected or modulated through an understanding of their cause. This requires familiarity with scanner design; theory of operation; and image acquisition. The purpose of this review article is to present the most relevant artifacts that arise in MRI scanner, to provide some physical background on the formation of artifacts, and to suggest strategies to reduce or avoid these artifacts. The most frequent artifacts that can occur during MRI scanning are Motion related artifacts; Para-magnetic artifacts; Phase Wrap artifacts; Frequency artifacts; Susceptibility artifacts; Clipping artefact; Chemical Shift artifact and "Zebra" artefact .    "n  

  4. Using DNA sequencing electrophoresis compression artifacts as reporters of stable mRNA structures affecting gene expression.

    Science.gov (United States)

    Kapoor, Divya; Chandrayan, Sanjeev Kumar; Ahmed, Shubbir; Guptasarma, Purnananda

    2007-11-01

    The formation of secondary structure in oligonucleotide DNA is known to lead to "compression" artifacts in electropherograms produced through DNA sequencing. Separately, the formation of secondary structure in mRNA is known to suppress translation; in particular, when such structures form in a region covered by the ribosome either during, or shortly after, initiation of translation. Here, we demonstrate how a DNA sequencing compression artifact provides important clues to the location(s) of translation-suppressing secondary structural elements in mRNA. Our study involves an engineered version of a gene sourced from Rhodothermus marinus encoding an enzyme called Cel12A. We introduced this gene into Escherichia coli with the intention of overexpressing it, but found that it expressed extremely poorly. Intriguingly, the gene displayed a remarkable compression artifact during DNA sequencing electrophoresis. Selected "designer" silent mutations destroyed the artifact. They also simultaneously greatly enhanced the expression of the cel12A gene, presumably by destroying stable mRNA structures that otherwise suppress translation. We propose that this method of finding problem mRNA sequences is superior to software-based analyses, especially if combined with low-temperature CE.

  5. Archaeology Through Computational Linguistics : Inscription Statistics Predict Excavation Sites of Indus Valley Artifacts

    NARCIS (Netherlands)

    Recchia, Gabriel L; Louwerse, Max M

    2015-01-01

    Computational techniques comparing co-occurrences of city names in texts allow the relative longitudes and latitudes of cities to be estimated algorithmically. However, these techniques have not been applied to estimate the provenance of artifacts with unknown origins. Here, we estimate the

  6. Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade

    Directory of Open Access Journals (Sweden)

    Sonal Kothari

    2013-01-01

    Full Text Available Background: Analysis of tissue biopsy whole-slide images (WSIs depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. Materials and Methods: We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa and ovarian serous adenocarcinoma (OvCa - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades. Results: When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI, 0.77 average true rate (ATR, 0.55 true positive rate (TPR, and 0.98 true negative rate (TNR; and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively. Conclusion: The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction

  7. Prediction of gas collection efficiency and particle collection artifact for atmospheric semivolatile organic compounds in multicapillary denuders.

    Science.gov (United States)

    Rowe, Mark D; Perlinger, Judith A

    2010-01-15

    A modeling approach is presented to predict the sorptive sampling collection efficiency of gaseous semivolatile organic compounds (SOCs) and the artifact caused by collection of particle-associated SOCs in multicapillary diffusion denuders containing polydimethylsiloxane (PDMS) stationary phase. Approaches are presented to estimate the equilibrium PDMS-gas partition coefficient (K(pdms)) from a solvation parameter model for any compound, and, for nonpolar compounds, from the octanol-air partition coefficient (K(oa)) if measured K(pdms) values are not available. These estimated K(pdms) values are compared with K(pdms) measured by gas chromatography. Breakthrough fraction was measured for SOCs collected from ambient air using high-flow (300 L min(-1)) and low-flow (13 L min(-1)) denuders under a range of sampling conditions (-10 to 25 degrees C; 11-100% relative humidity). Measured breakthrough fraction agreed with predictions based on frontal chromatography theory using K(pdms) and equations of Golay, Lövkvist and Jönsson within measurement precision. Analytes included hexachlorobenzene, 144 polychlorinated biphenyl congeners, and polybrominated diphenyl ethers 47 and 99. Atmospheric particle transmission efficiency was measured for the high-flow denuder (0.037-6.3 microm diameter), and low-flow denuder (0.015-3.1 microm diameter). Particle transmission predicted using equations of Gormley and Kennedy, Pich, and a modified filter model, agreed within measurement precision (high-flow denuder) or were slightly greater than (low-flow denuder) measured particle transmission. As an example application of the model, breakthrough volume and particle collection artifact for the two denuder designs were predicted as a function of K(oa) for nonpolar SOCs. The modeling approach is a necessary tool for the design and use of denuders for sorptive sampling with PDMS stationary phase.

  8. Facts and artifacts in studies of gene expression in aneuploids and sex chromosomes.

    Science.gov (United States)

    Birchler, James A

    2014-10-01

    Studies of gene expression in aneuploids have often made the assumption that measurements of RNA abundance from the varied chromosome will establish whether there is a dosage effect or compensation. Typical procedures of RNA isolation and use of equal amounts of RNA for quantitative estimates will not measure the total transcriptome size nor the absolute expression levels per cell. Use of internal endogenous standards or averages from unvaried chromosomes for normalizations makes the assumption that there are no global modulations across the genome. However, studies that use controls to test these assumptions reveal that there are in fact often modulations on all chromosomes. The same caveats apply to gene expression studies of sex chromosomes, which also involve changes in dosage of a small portion of the genome. Here, we describe some of the pitfalls of studies of aneuploidy and sex chromosome gene expression and review methods that have been used to avoid them.

  9. The Prediction of Rice Gene by Fgenesh

    Institute of Scientific and Technical Information of China (English)

    ZHANG Sheng-li; LI Dong-fang; ZHANG Gai-sheng; WANG Jun-wei; NIU Na

    2008-01-01

    This study has been carried out to give some scientific reasons for genome annotation, shorten the annotating time, and improve the results of gene prediction. Taking the sequence of the 6th chromosome, which has more length sequences than others, of Oryza sativa L. ssp. japonica cv. Nipponbare as analysis data in this research, the gene prediction of monocots module, rice, has been done by using Fgenesh ver. 2.0, and the predicting results have been explored particularly by bioinformatics methods. Results showed that the number of predicted genes for this chromosome was very close to the number of TIGR annotated genes. The majority of the predicted genes were multi-exon genes which had a percentage of 77.52. Length range was very big in the predicted genes. According to the significant match number, multi-exon genes can be predicted more veracity than single exon genes and the support can be reached up to 100% by TIGR annotation and up to 78% by cDNA. From the angle of predicted exons location of multi-exon genes, the internal exons and last exons had a high support of cDNA. The length of internal exons was relatively short in high (>95% length, >78% similarity) cDNA and/or TIGR annotation support multi-exon genes, but the first exons and last exons were on the reverse. The majority of single exon genes which had more than 95% in length, and 78% in similarity support by cDNA and/or TIGR annotation was relatively short in length. From the angle of exon number, the majority of the multi-exon genes of high (> 95% length, > 78% similarity) cDNA and/or TIGR annotation support had no more than 5 exon number. It was concluded that the rice gene prediction by Fgenesh was very good but needed modification manually to some extent according to cDNA support after aligning the predicting sequence of genes with cDNA database of rice.

  10. Microvariation artifacts introduced by PCR and cloning of closely related 16S rRNA gene sequences

    NARCIS (Netherlands)

    Speksnijder, A.G.C.L.; Kowalchuk, G.A.; Jong, S. de; Kline, E.; Stephen, J.R.; Laanbroek, H.J.

    2001-01-01

    A defined template mixture of seven closely related 16S-rDNA clones was used in a PCR-cloning experiment to assess and track sources of artifactual sequence variation in 16S rDNA clone libraries. At least 14% of the recovered clones contained aberrations. Artifact sources were polymerase errors, a m

  11. Microvariation Artifacts Introduced by PCR and Cloning of Closely Related 16S rRNA Gene Sequences

    NARCIS (Netherlands)

    Speksnijder, A.G.C.L.; Kowalchuk, G.A.; Jong, de S.; Kline, E.; Stephen, J.R.; Laanbroek, H.J.

    2001-01-01

    A defined template mixture of seven closely related 16S-rDNA clones was used in a PCR-cloning experiment to assess and track sources of artifactual sequence variation in 16S rDNA clone libraries. At least 14% of the recovered clones contained aberrations. Artifact sources were polymerase errors, a m

  12. Predicting gene expression from sequence: a reexamination.

    Directory of Open Access Journals (Sweden)

    Yuan Yuan

    2007-11-01

    Full Text Available Although much of the information regarding genes' expressions is encoded in the genome, deciphering such information has been very challenging. We reexamined Beer and Tavazoie's (BT approach to predict mRNA expression patterns of 2,587 genes in Saccharomyces cerevisiae from the information in their respective promoter sequences. Instead of fitting complex Bayesian network models, we trained naïve Bayes classifiers using only the sequence-motif matching scores provided by BT. Our simple models correctly predict expression patterns for 79% of the genes, based on the same criterion and the same cross-validation (CV procedure as BT, which compares favorably to the 73% accuracy of BT. The fact that our approach did not use position and orientation information of the predicted binding sites but achieved a higher prediction accuracy, motivated us to investigate a few biological predictions made by BT. We found that some of their predictions, especially those related to motif orientations and positions, are at best circumstantial. For example, the combinatorial rules suggested by BT for the PAC and RRPE motifs are not unique to the cluster of genes from which the predictive model was inferred, and there are simpler rules that are statistically more significant than BT's ones. We also show that CV procedure used by BT to estimate their method's prediction accuracy is inappropriate and may have overestimated the prediction accuracy by about 10%.

  13. Artifacts in digital radiography

    Energy Technology Data Exchange (ETDEWEB)

    Min, Jung Whan [Dept. of Radiological Technology, Shin Gu University, Sungnam (Korea, Republic of); Kim, Jung Min [Dept. of Radiological Technology, Korea University, Seoul (Korea, Republic of); Jeong, Hoi Woun [Dept. of Radiological Technology, Beakseok Culture University, Cheonan (Korea, Republic of)

    2015-12-15

    Digital Radiography is a big part of diagnostic radiology. Because uncorrected digital radiography image supported false effect of Patient’s health care. We must be manage the correct digital radiography image. Thus, the artifact images can have effect to make a wrong diagnosis. We report types of occurrence by analyzing the artifacts that occurs in digital radiography system. We had collected the artifacts occurred in digital radiography system of general hospital from 2007 to 2014. The collected data had analyzed and then had categorize as the occurred causes. The artifacts could be categorized by hardware artifacts, software artifacts, operating errors, system artifacts, and others. Hardware artifact from a Ghost artifact that is caused by lag effect occurred most frequently. The others cases are the artifacts caused by RF noise and foreign body in equipments. Software artifacts are many different types of reasons. The uncorrected processing artifacts and the image processing error artifacts occurred most frequently. Exposure data recognize (EDR) error artifacts, the processing error of commissural line, and etc., the software artifacts were caused by various reasons. Operating artifacts were caused when the user did not have the full understanding of the digital medical image system. System artifacts had appeared the error due to DICOM header information and the compression algorithm. The obvious artifacts should be re-examined, and it could result in increasing the exposure dose of the patient. The unclear artifact leads to a wrong diagnosis and added examination. The ability to correctly determine artifact are required. We have to reduce the artifact occurrences by understanding its characteristic and providing sustainable education as well as the maintenance of the equipments.

  14. Artifacts as Conventional Objects

    Science.gov (United States)

    Siegel, Deborah R.; Callanan, Maureen A.

    2007-01-01

    What underlies children's understanding of artifacts? Studies suggest that beginning around age 7, people reason about artifacts in terms of the inventor's purpose--termed "the design stance." Our two studies emphasize another component of artifact understanding--the cultural nature of artifacts--by demonstrating people's sensitivity to an…

  15. Ultrasound Artifacts - Part 1.

    Science.gov (United States)

    Bönhof, J A

    2016-04-01

    Knowledge of artifacts is essential for the competent use of ultrasound. Artifacts are method-based and should be differentiated from image errors of another genesis. They are logical and occur because the conditions required for image generation do not fully correspond to the reality. Artifacts occur due to disregard of the true dimensions of sound lobes (slice-thickness artifacts and bow artifacts, range ambiguities) and due to different types of mirroring with different appearances. There are also comet-tail-like artifacts such as comet-tail and ring-down artifacts.

  16. PET/CT Artifacts

    OpenAIRE

    Blodgett, Todd M.; Mehta, Ajeet S.; Mehta, Amar S.; Laymon, Charles M; Carney, Jonathan; Townsend, David W.

    2011-01-01

    There are several artifacts encountered in PET/CT imaging, including attenuation correction (AC) artifacts associated with using CT for attenuation correction. Several artifacts can mimic a 2-deoxy-2-[18F] fluoro-D-glucose (FDG) avid malignant lesions and therefore recognition of these artifacts is clinically relevant. Our goal was to identify and characterize these artifacts and also discuss some protocol variables that may affect image quality in PET/CT.

  17. Dynamics in artifact ecologies

    DEFF Research Database (Denmark)

    Bødker, Susanne; Klokmose, Clemens Nylandsted

    2012-01-01

    We increasingly interact with multiple interactive artifacts with overlapping capabilities during our daily activities. It has previously been shown that the use of an interactive artifact cannot be understood in isolation, but artifacts must be understood as part of an artifact ecology, where ar...... in artifact ecologies cannot be understood as static, instead they evolve dynamically over time. We provide activity theory-based concepts to explain these dynamics....

  18. Mesoscale hybrid calibration artifact

    Science.gov (United States)

    Tran, Hy D.; Claudet, Andre A.; Oliver, Andrew D.

    2010-09-07

    A mesoscale calibration artifact, also called a hybrid artifact, suitable for hybrid dimensional measurement and the method for make the artifact. The hybrid artifact has structural characteristics that make it suitable for dimensional measurement in both vision-based systems and touch-probe-based systems. The hybrid artifact employs the intersection of bulk-micromachined planes to fabricate edges that are sharp to the nanometer level and intersecting planes with crystal-lattice-defined angles.

  19. Gene Prediction Using Multinomial Probit Regression with Bayesian Gene Selection

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2004-01-01

    Full Text Available A critical issue for the construction of genetic regulatory networks is the identification of network topology from data. In the context of deterministic and probabilistic Boolean networks, as well as their extension to multilevel quantization, this issue is related to the more general problem of expression prediction in which we want to find small subsets of genes to be used as predictors of target genes. Given some maximum number of predictors to be used, a full search of all possible predictor sets is combinatorially prohibitive except for small predictors sets, and even then, may require supercomputing. Hence, suboptimal approaches to finding predictor sets and network topologies are desirable. This paper considers Bayesian variable selection for prediction using a multinomial probit regression model with data augmentation to turn the multinomial problem into a sequence of smoothing problems. There are multiple regression equations and we want to select the same strongest genes for all regression equations to constitute a target predictor set or, in the context of a genetic network, the dependency set for the target. The probit regressor is approximated as a linear combination of the genes and a Gibbs sampler is employed to find the strongest genes. Numerical techniques to speed up the computation are discussed. After finding the strongest genes, we predict the target gene based on the strongest genes, with the coefficient of determination being used to measure predictor accuracy. Using malignant melanoma microarray data, we compare two predictor models, the estimated probit regressors themselves and the optimal full-logic predictor based on the selected strongest genes, and we compare these to optimal prediction without feature selection.

  20. Breast Imaging Artifacts.

    Science.gov (United States)

    Odle, Teresa G

    2015-01-01

    Artifacts appear on breast images for a number of reasons. Radiologic technologists play an important role in identifying artifacts that can help or hinder breast cancer diagnosis and in minimizing artifacts that degrade image quality. This article describes various artifacts that occur in breast imaging, along with their causes. The article focuses on artifacts in mammography, with a heavy emphasis on digital mammography, and on magnetic resonance imaging of the breast. Artifacts in ultrasonography of the breast, digital breast tomosynthesis, and positron emission mammography also are discussed.

  1. Efficacy evaluation of retrospectively applying the Varian normal breathing predictive filter for volume definition and artifact reduction in 4D CT lung patients.

    Science.gov (United States)

    Malone, Ciaran; Rock, Luke; Skourou, Christina

    2014-05-08

    Phase-based sorting of four-dimensional computed tomography (4D CT) datasets is prone to image artifacts due to patient's breathing irregularities that occur during the image acquisition. The purpose of this study is to investigate the effect of the Varian normal breathing predictive filter (NBPF) as a retrospective phase-sorting parameter in 4D CT. Ten 4D CT lung cancer datasets were obtained. The volumes of all tumors present, as well as the total lung volume, were calculated on the maximum intensity projection (MIP) images as well as each individual phase image. The NBPF was varied retrospectively within the available range, and changes in volume and image quality were recorded. The patients' breathing trace was analysed and the magnitude and location of any breathing irregularities were correlated to the behavior of the NBPF. The NBPF was found to have a considerable effect on the quality of the images in MIP and single-phase datasets. When used appropriately, the NBPF is shown to have the ability to account for and correct image artifacts. However, when turned off (0%) or set above a critical level (approximately 40%), it resulted in erroneous volume reconstructions with variations in tumor volume up to 26.6%. Those phases associated with peak inspiration were found to be more susceptible to changes in the NBPF. The NBPF settings selected prior to exporting the breathing trace for patients evaluated using 4D CT directly affect the accuracy of the targeting and volume estimation of lung tumors. Recommendations are made to address potential errors in patient anatomy introduced by breathing irregularities, specifically deep breath or cough irregularities, by implementing the proper settings and use of this tool.

  2. Integrating Gene Ontology and Blast to predict gene functions

    Institute of Scientific and Technical Information of China (English)

    WANG Cheng-gang; MO Zhi-hong

    2007-01-01

    A GoBlast system was built to predict gene function by integrating Blast search and Gene Ontology (GO) annotations together. The operation system was based on Debian Linux 3.1, with Apache as the web server and Mysql database as the data storage system. FASTA files with GO annotations were taken as the sequence source for blast alignment, which were formatted by wu-formatdb program. The GoBlast system includes three Bioperl modules in Perl: a data input module, a data process module and a data output module. A GoBlast query starts with an amino acid or nucleotide sequence. It ends with an output in an html page, presenting high scoring gene products which are of a high homology to the queried sequence and listing associated GO terms beside respective gene poducts. A simple click on a GO term leads to the detailed explanation of the specific gene function. This avails gene function prediction by Blast. GoBlast can be a very useful tool for functional genome research and is available for free at http://bioq.org/goblast.

  3. Predicting metastasized seminoma using gene expression.

    Science.gov (United States)

    Ruf, Christian G; Linbecker, Michael; Port, Matthias; Riecke, Armin; Schmelz, Hans U; Wagner, Walter; Meineke, Victor; Abend, Michael

    2012-07-01

    Treatment options for testis cancer depend on the histological subtype as well as on the clinical stage. An accurate staging is essential for correct treatment. The 'golden standard' for staging purposes is CT, but occult metastasis cannot be detected with this method. Currently, parameters such as primary tumour size, vessel invasion or invasion of the rete testis are used for predicting occult metastasis. Last year the association of these parameters with metastasis could not be validated in a new independent cohort. Gene expression analysis in testis cancer allowed discrimination between the different histological subtypes (seminoma and non-seminoma) as well as testis cancer and normal testis tissue. In a two-stage study design we (i) screened the whole genome (using human whole genome microarrays) for candidate genes associated with the metastatic stage in seminoma and (ii) validated and quantified gene expression of our candidate genes (real-time quantitative polymerase chain reaction) on another independent group. Gene expression measurements of two of our candidate genes (dopamine receptor D1 [DRD1] and family with sequence similarity 71, member F2 [FAM71F2]) examined in primary testis cancers made it possible to discriminate the metastasis status in seminoma. The discriminative ability of the genes exceeded the predictive significance of currently used histological/pathological parameters. Based on gene expression analysis the present study provides suggestions for improved individual decision making either in favour of early adjuvant therapy or increased surveillance. To evaluate the usefulness of gene expression profiling for predicting metastatic status in testicular seminoma at the time of first diagnosis compared with established clinical and pathological parameters. Total RNA was isolated from testicular tumours of metastasized patients (12 patients, clinical stage IIa-III), non-metastasized patients (40, clinical stage I) and adjacent 'normal' tissue

  4. Gene function prediction based on the Gene Ontology hierarchical structure.

    Science.gov (United States)

    Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

    2014-01-01

    The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

  5. Genomic Prediction of Gene Bank Wheat Landraces

    Directory of Open Access Journals (Sweden)

    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

  6. Artifacts in musculoskeletal ultrasonography.

    Science.gov (United States)

    Taljanovic, Mihra S; Melville, David M; Scalcione, Luke R; Gimber, Lana H; Lorenz, Eileen J; Witte, Russell S

    2014-02-01

    During the past 2 decades, high-resolution ultrasonography (US) has been increasingly utilized in the diagnosis of musculoskeletal trauma and diseases with results comparable with MR imaging. US has an advantage over other cross-sectional modalities in many circumstances due to its superior spatial resolution and ability to allow dynamic assessment. When performing musculoskeletal US, the examiner has to be knowledgeable in the complex anatomy of the musculoskeletal system and US imaging technique. Additionally, he or she must be familiar with several common imaging artifacts in musculoskeletal US that may be mistaken for pathology, as well as several artifacts that frequently accompany pathologic conditions. These artifacts may occur with both B-mode gray-scale and Doppler imaging. In this article, we discuss common artifacts seen in musculoskeletal US and techniques to avoid or minimize these artifacts during clinical US examinations.

  7. Artifacts and essentialism.

    Science.gov (United States)

    Gelman, Susan A

    2013-09-01

    Psychological essentialism is an intuitive folk belief positing that certain categories have a non-obvious inner "essence" that gives rise to observable features. Although this belief most commonly characterizes natural kind categories, I argue that psychological essentialism can also be extended in important ways to artifact concepts. Specifically, concepts of individual artifacts include the non-obvious feature of object history, which is evident when making judgments regarding authenticity and ownership. Classic examples include famous works of art (e.g., the Mona Lisa is authentic because of its provenance), but ordinary artifacts likewise receive value from their history (e.g., a worn and tattered blanket may have special value if it was one's childhood possession). Moreover, in some cases, object history may be thought to have causal effects on individual artifacts, much as an animal essence has causal effects. I review empirical support for these claims and consider the implications for both artifact concepts and essentialism. This perspective suggests that artifact concepts cannot be contained in a theoretical framework that focuses exclusively on similarity or even function. Furthermore, although there are significant differences between essentialism of natural kinds and essentialism of artifact individuals, the commonalities suggest that psychological essentialism may not derive from folk biology but instead may reflect more domain-general perspectives on the world.

  8. Artifacts in diagnostic ultrasound

    OpenAIRE

    Hindi A; Peterson C; Barr RG

    2013-01-01

    Ammar Hindi,1 Cynthia Peterson,2 Richard G Barr3,41Department of Radiology, University Hospitals of Cleveland, Cleveland, Ohio, USA; 2Department of Allied Health, Kent State University, Salem, OH, USA; 3Department of Radiology, Northeastern Ohio Medical University, Rootstown, OH, USA; 4Radiology Consultants, Youngstown, OH, USAAbstract: Ultrasound artifacts are encountered daily in clinical practice and may be a source of confusion on interpretation. Some artifacts arise secondary to improper...

  9. Bioinformatic prediction and functional characterization of human KIAA0100 gene

    OpenAIRE

    He Cui; Xi Lan; Shemin Lu; Fujun Zhang; Wanggang Zhang

    2017-01-01

    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...

  10. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    Directory of Open Access Journals (Sweden)

    Hao Ke

    2011-11-01

    Full Text Available Abstract Background 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. Methods 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. Results 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. Conclusion 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.

  11. Gene expression profiling predicts the development of oral cancer.

    Science.gov (United States)

    Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K; Papadimitrakopoulou, Vassiliki A; Feng, Lei; Lee, J Jack; Kim, Edward S; Ki Hong, Waun; Mao, Li

    2011-02-01

    Patients with oral premalignant lesion (OPL) have a high risk of developing oral cancer. Although certain risk factors, such as smoking status and histology, are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develop multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinicopathologic risk factors. On the basis of the gene expression profile data, we also identified 2,182 transcripts significantly associated with oral cancer risk-associated genes (P value oral cancer risk. In multiple independent data sets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. ©2011 AACR.

  12. Using intron position conservation for homology-based gene prediction.

    Science.gov (United States)

    Keilwagen, Jens; Wenk, Michael; Erickson, Jessica L; Schattat, Martin H; Grau, Jan; Hartung, Frank

    2016-05-19

    Annotation of protein-coding genes is very important in bioinformatics and biology and has a decisive influence on many downstream analyses. Homology-based gene prediction programs allow for transferring knowledge about protein-coding genes from an annotated organism to an organism of interest.Here, we present a homology-based gene prediction program called GeMoMa. GeMoMa utilizes the conservation of intron positions within genes to predict related genes in other organisms. We assess the performance of GeMoMa and compare it with state-of-the-art competitors on plant and animal genomes using an extended best reciprocal hit approach. We find that GeMoMa often makes more precise predictions than its competitors yielding a substantially increased number of correct transcripts. Subsequently, we exemplarily validate GeMoMa predictions using Sanger sequencing. Finally, we use RNA-seq data to compare the predictions of homology-based gene prediction programs, and find again that GeMoMa performs well.Hence, we conclude that exploiting intron position conservation improves homology-based gene prediction, and we make GeMoMa freely available as command-line tool and Galaxy integration. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  13. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis

    OpenAIRE

    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 numb...

  14. Systematic Characterization and Prediction of Human Hypertension Genes.

    Science.gov (United States)

    Li, Yan-Hui; Zhang, Gai-Gai; Wang, Nanping

    2017-02-01

    Hypertension is a major cardiovascular risk factor and accounts for a large part of cardiovascular mortality. In this work, we analyzed the properties of hypertension genes and found that when compared with genes not yet known to be involved in hypertension regulation, known hypertension genes display distinguishing features: (1) hypertension genes tend to be located at network center; (2) hypertension genes tend to interact with each other; and (3) hypertension genes tend to enrich in certain biological processes and show certain phenotypes. Based on these features, we developed a machine-learning algorithm to predict new hypertension genes. One hundred and seventy-seven candidates were predicted with a posterior probability >0.9. Evidence supporting 17 of the predictions has been found. © 2016 American Heart Association, Inc.

  15. Facts in artifacts

    Directory of Open Access Journals (Sweden)

    P R Bindhu

    2013-01-01

    Full Text Available Examination of microscopic sections of animal tissues reveals facts which are not always related to its normal histology or pathology. Processing of tissue specimens consists of lengthy procedures from the stage of surgical removal to the stained and mounted microscopic sections. Defects are common in tissue sections as a result of faulty procedures. These defects are referred to as artifacts. They lead to misinterpretation of histopathological diagnosis but at times they throw limelight into diagnosis. This paper attempts to put together all the facts regarding the various artifacts that are encountered in histopathology.

  16. Controlling Modelling Artifacts

    DEFF Research Database (Denmark)

    Smith, Michael James Andrew; Nielson, Flemming; Nielson, Hanne Riis

    2011-01-01

    as the high-level model, so that they can be directly compared. There are two key ideas in our approach — a temporal abstraction, where we only look at the state of the system at certain observable points in time, and a spatial abstraction, where we project onto a smaller state space that summarises...... artifacts that were inadvertently introduced. In this paper, we propose a novel methodology to reason about modelling artifacts, given a detailed model and a highlevel (more abstract) model of the same system. By a series of automated abstraction steps, we lift the detailed model to the same state space...

  17. A Brief Review of Computational Gene Prediction Methods

    Institute of Scientific and Technical Information of China (English)

    Zhuo Wang; Yazhu Chen; Yixue Li

    2004-01-01

    With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.

  18. The Information Systems Artifact

    DEFF Research Database (Denmark)

    Chatterjee, Surtirtha; Xiao, Xiao; Elbanna, Amany

    2017-01-01

    Passionate debates regarding the defining characteristic of the “IT artifact” continue. Such debates, and also the lack of explicit consideration of the “information” element in the IT artifact, motivate us to propose a revised conception, drawing upon concepts from General Systems Theory (GST...

  19. The Information Systems Artifact

    DEFF Research Database (Denmark)

    Chatterjee, Surtirtha; Xiao, Xiao; Elbanna, Amany

    2017-01-01

    Passionate debates regarding the defining characteristic of the “IT artifact” continue. Such debates, and also the lack of explicit consideration of the “information” element in the IT artifact, motivate us to propose a revised conception, drawing upon concepts from General Systems Theory (GST...

  20. Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression.

    Science.gov (United States)

    Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression Exposure to many drugs and environmentally-relevant chemicals can cause adverse outcomes. These adverse outcomes, such as cancer, have been linked to mol...

  1. In silico network topology-based prediction of gene essentiality

    CERN Document Server

    da Silva, Joao Paulo Muller; Mombach, Jose Carlos Merino; Vieira, Renata; da Silva, Jose Guliherme Camargo; Lemke, Ney; Sinigaglia, Marialva

    2007-01-01

    The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision tree-based machine learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes...

  2. Gene and translation initiation site prediction in metagenomic sequences

    Energy Technology Data Exchange (ETDEWEB)

    Hyatt, Philip Douglas [ORNL; LoCascio, Philip F [ORNL; Hauser, Loren John [ORNL; Uberbacher, Edward C [ORNL

    2012-01-01

    Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data. We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translation initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements.

  3. GenePRIMP: A GENE PRediction IMprovement Pipeline for Prokaryotic genomes

    Energy Technology Data Exchange (ETDEWEB)

    Pati, Amrita; Ivanova, Natalia N.; Mikhailova, Natalia; Ovchinnikova, Galina; Hooper, Sean D.; Lykidis, Athanasios; Kyrpides, Nikos C.

    2010-04-01

    We present 'gene prediction improvement pipeline' (GenePRIMP; http://geneprimp.jgi-psf.org/), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missed genes and split genes. We found that manual curation of gene models using the anomaly reports generated by GenePRIMP improved their quality, and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome-sequencing and annotation technologies.

  4. Controlling Modelling Artifacts

    DEFF Research Database (Denmark)

    Smith, Michael James Andrew; Nielson, Flemming; Nielson, Hanne Riis

    2011-01-01

    the possible configurations of the system (for example, by counting the number of components in a certain state). We motivate our methodology with a case study of the LMAC protocol for wireless sensor networks. In particular, we investigate the accuracy of a recently proposed high-level model of LMAC......When analysing the performance of a complex system, we typically build abstract models that are small enough to analyse, but still capture the relevant details of the system. But it is difficult to know whether the model accurately describes the real system, or if its behaviour is due to modelling...... artifacts that were inadvertently introduced. In this paper, we propose a novel methodology to reason about modelling artifacts, given a detailed model and a highlevel (more abstract) model of the same system. By a series of automated abstraction steps, we lift the detailed model to the same state space...

  5. Small Artifacts - Big Technologies

    DEFF Research Database (Denmark)

    Kreiner, Kristian

    2005-01-01

    The computer IC is the heart of the information and telecommunication technology. It is a tiny artifact, but with incredible organizing powers. We use this physical artifact as the location for studying central problems of the knowledge economy. First, the paper describes the history of chip design...... and the emergence of the technological community involved in designing and manufacturing computer chips. The community is structured in a way that reflects the underlying physical nature silicon and the numerous other materials and chemicals involved. But it also reflects the human agency of defining new projects...... instrument. It is found that technological progress is not hindered, but rather aided by the use of imperfect principles, abstractions and representations of reality. The power of such imperfections is discussed and generalized....

  6. Metrological multispherical freeform artifact

    Science.gov (United States)

    Blobel, Gernot; Wiegmann, Axel; Siepmann, Jens; Schulz, Michael

    2016-07-01

    Precisely known artifacts are required to characterize the accuracy of asphere and freeform measuring instruments. To this end the best knowledge of the surface characteristics in conjunction with a low measurement uncertainty are necessary. Because this is a challenging task for typical freeform surfaces used in optical systems, the concept of "metrological" artifacts is introduced. We have developed a multispherical freeform artifact for performance tests of tactile touch probe and contact-free optical measuring systems. The measurement accuracy of the complete form and the deviation from calibrated spherical sections can thus be determined. The radius calibration of multiple spherical sections is performed with an extended radius measuring procedure by interferometry. Evaluated surface forms of different measuring methods and the radii determined can be compared to each other. In this study, a multispherical freeform specimen made of copper, with two differing radii, has been measured by two optical measuring methods, a full field measuring tilted-wave interferometer and a high accuracy cylinder coordinate measuring machine with an optical probe. The surface form measurements are evaluated and compared, and the radii determined are compared to the results of a radius measurement bench.

  7. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    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.

  8. Predicting Gene Structures from Multiple RT-PCR Tests

    Science.gov (United States)

    Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

    It has been demonstrated that the use of additional information such as ESTs and protein homology can significantly improve accuracy of gene prediction. However, many sources of external information are still being omitted from consideration. Here, we investigate the use of product lengths from RT-PCR experiments in gene finding. We present hardness results and practical algorithms for several variants of the problem and apply our methods to a real RT-PCR data set in the Drosophila genome. We conclude that the use of RT-PCR data can improve the sensitivity of gene prediction and locate novel splicing variants.

  9. An Additive Manufacturing Test Artifact

    Science.gov (United States)

    Moylan, Shawn; Slotwinski, John; Cooke, April; Jurrens, Kevin; Donmez, M Alkan

    2014-01-01

    A test artifact, intended for standardization, is proposed for the purpose of evaluating the performance of additive manufacturing (AM) systems. A thorough analysis of previously proposed AM test artifacts as well as experience with machining test artifacts have inspired the design of the proposed test artifact. This new artifact is designed to provide a characterization of the capabilities and limitations of an AM system, as well as to allow system improvement by linking specific errors measured in the test artifact to specific sources in the AM system. The proposed test artifact has been built in multiple materials using multiple AM technologies. The results of several of the builds are discussed, demonstrating how the measurement results can be used to characterize and improve a specific AM system. PMID:26601039

  10. An Additive Manufacturing Test Artifact.

    Science.gov (United States)

    Moylan, Shawn; Slotwinski, John; Cooke, April; Jurrens, Kevin; Donmez, M Alkan

    2014-01-01

    A test artifact, intended for standardization, is proposed for the purpose of evaluating the performance of additive manufacturing (AM) systems. A thorough analysis of previously proposed AM test artifacts as well as experience with machining test artifacts have inspired the design of the proposed test artifact. This new artifact is designed to provide a characterization of the capabilities and limitations of an AM system, as well as to allow system improvement by linking specific errors measured in the test artifact to specific sources in the AM system. The proposed test artifact has been built in multiple materials using multiple AM technologies. The results of several of the builds are discussed, demonstrating how the measurement results can be used to characterize and improve a specific AM system.

  11. The Human-Artifact Model

    DEFF Research Database (Denmark)

    Bødker, Susanne; Klokmose, Clemens Nylandsted

    2011-01-01

    Although devices of all shapes and sizes currently dominate the technological landscape, human–computer interaction (HCI) as a field is not yet theoretically equipped to match this reality. In this article we develop the human–artifact model, which has its roots in activity theoretical HCI....... By reinterpreting the activity theoretical foundation, we present a framework that helps addressing the analysis of individual interactive artifacts while embracing that they are part of a larger ecology of artifacts. We show how the human–artifact model helps structuring the understanding of an artifact's action......-possibilities in relation to the artifact ecology surrounding it. Essential to the model is that it provides four interconnected levels of analysis and addresses the possibilities and problems at these four levels. Artifacts and their use are constantly developing, and we address development in, and of, use. The framework...

  12. Engineering genes for predictable protein expression.

    Science.gov (United States)

    Gustafsson, Claes; Minshull, Jeremy; Govindarajan, Sridhar; Ness, Jon; Villalobos, Alan; Welch, Mark

    2012-05-01

    The DNA sequence used to encode a polypeptide can have dramatic effects on its expression. Lack of readily available tools has until recently inhibited meaningful experimental investigation of this phenomenon. Advances in synthetic biology and the application of modern engineering approaches now provide the tools for systematic analysis of the sequence variables affecting heterologous expression of recombinant proteins. We here discuss how these new tools are being applied and how they circumvent the constraints of previous approaches, highlighting some of the surprising and promising results emerging from the developing field of gene engineering.

  13. Prediction of anther-expressed gene resulation in Arabidopsis

    Institute of Scientific and Technical Information of China (English)

    HUANG JiFeng; YANG JingJin; WANG Guan; YU QingBo; YANG ZhongNan

    2008-01-01

    Anther development in Arabidopsis, a popular model plant for plant biology and genetics, is controlled by a complex gene network. Despite the extensive use of this genus for genetic research, little is known about its regulatory network. In this paper, the direct transcriptional regulatory relationships between genes expressed in Arabidopsis anther development were predicted with an integrated bioinformatic method that combines mining of microarray data with promoter analysis. A total of 7710 transcription factor-gene pairs were obtained. The 80 direct regulatory relationships demonstrating the highest con-fidence were screened from the initial 7710 pairs; three of the 80 were validated by previous experi-ments. The results indicate that our predicted results were reliable. The regulatory relationships re-vealed by this research and described in this paper may facilitate further investigation of the molecular mechanisms of anther development. The bioinformatic method used in this work can also be applied to the prediction of gene regulatory relationships in other organisms.

  14. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    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.

  15. Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG

    OpenAIRE

    Zhen Li; Bi-Qing Li; Min Jiang; Lei Chen; Jian Zhang; Lin Liu; Tao Huang

    2013-01-01

    One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance...

  16. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Directory of Open Access Journals (Sweden)

    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.

  17. The MAOA gene predicts happiness in women.

    Science.gov (United States)

    Chen, Henian; Pine, Daniel S; Ernst, Monique; Gorodetsky, Elena; Kasen, Stephanie; Gordon, Kathy; Goldman, David; Cohen, Patricia

    2013-01-10

    Psychologists, quality of life and well-being researchers have grown increasingly interested in understanding the factors that are associated with human happiness. Although twin studies estimate that genetic factors account for 35-50% of the variance in human happiness, knowledge of specific genes is limited. However, recent advances in molecular genetics can now provide a window into neurobiological markers of human happiness. This investigation examines association between happiness and monoamine oxidase A (MAOA) genotype. Data were drawn from a longitudinal study of a population-based cohort, followed for three decades. In women, low expression of MAOA (MAOA-L) was related significantly to greater happiness (0.261 SD increase with one L-allele, 0.522 SD with two L-alleles, P=0.002) after adjusting for the potential effects of age, education, household income, marital status, employment status, mental disorder, physical health, relationship quality, religiosity, abuse history, recent negative life events and self-esteem use in linear regression models. In contrast, no such association was found in men. This new finding may help explain the gender difference on happiness and provide a link between MAOA and human happiness. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. Global discriminative learning for higher-accuracy computational gene prediction.

    Directory of Open Access Journals (Sweden)

    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.

  19. A predictive approach to identify genes differentially expressed

    Science.gov (United States)

    Saraiva, Erlandson F.; Louzada, Francisco; Milan, Luís A.; Meira, Silvana; Cobre, Juliana

    2012-10-01

    The main objective of gene expression data analysis is to identify genes that present significant changes in expression levels between a treatment and a control biological condition. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating credibility intervals from predictive densities which are constructed using sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained indicate that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a publicly available data set on Escherichia coli bacteria.

  20. 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

  1. Bioinformatic prediction and functional characterization of human KIAA0100 gene

    Directory of Open Access Journals (Sweden)

    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.

  2. A network approach to predict pathogenic genes for Fusarium graminearum.

    Directory of Open Access Journals (Sweden)

    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

  3. A Network Approach to Predict Pathogenic Genes for Fusarium graminearum

    Science.gov (United States)

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

    2010-01-01

    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

  4. Using effective subnetworks to predict selected properties of gene networks.

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    Gemunu H Gunaratne

    Full Text Available BACKGROUND: Difficulties associated with implementing gene therapy are caused by the complexity of the underlying regulatory networks. The forms of interactions between the hundreds of genes, proteins, and metabolites in these networks are not known very accurately. An alternative approach is to limit consideration to genes on the network. Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. Furthermore, error estimates of the network make verifiable predictions impossible. METHODOLOGY/PRINCIPAL FINDINGS: Here, we propose an alternative approach. Rather than attempting to derive an accurate model of the network, we ask what questions can be addressed using lower dimensional, highly simplified models. More importantly, is it possible to use such robust features in applications? We first identify a small group of genes that can be used to affect changes in other nodes of the network. The reduced effective empirical subnetwork (EES can be computed using steady state measurements on a small number of genetically perturbed systems. We show that the EES can be used to make predictions on expression profiles of other mutants, and to compute how to implement pre-specified changes in the steady state of the underlying biological process. These assertions are verified in a synthetic influence network. We also use previously published experimental data to compute the EES associated with an oxygen deprivation network of E.coli, and use it to predict gene expression levels on a double mutant. The predictions are significantly different from the experimental results for less than of genes. CONCLUSIONS/SIGNIFICANCE: The constraints imposed by gene expression levels of mutants can be used to address a selected set of questions about a gene network.

  5. Ontology-Based Prediction and Prioritization of Gene Functional Annotations.

    Science.gov (United States)

    Chicco, Davide; Masseroli, Marco

    2016-01-01

    Genes and their protein products are essential molecular units of a living organism. The knowledge of their functions is key for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. The association of a gene or protein with its functions, described by controlled terms of biomolecular terminologies or ontologies, is named gene functional annotation. Very many and valuable gene annotations expressed through terminologies and ontologies are available. Nevertheless, they might include some erroneous information, since only a subset of annotations are reviewed by curators. Furthermore, they are incomplete by definition, given the rapidly evolving pace of biomolecular knowledge. In this scenario, computational methods that are able to quicken the annotation curation process and reliably suggest new annotations are very important. Here, we first propose a computational pipeline that uses different semantic and machine learning methods to predict novel ontology-based gene functional annotations; then, we introduce a new semantic prioritization rule to categorize the predicted annotations by their likelihood of being correct. Our tests and validations proved the effectiveness of our pipeline and prioritization of predicted annotations, by selecting as most likely manifold predicted annotations that were later confirmed.

  6. Information theory applied to the sparse gene ontology annotation network to predict novel gene function

    Science.gov (United States)

    Tao, Ying; Li, Jianrong

    2010-01-01

    Motivation Despite advances in the gene annotation process, the functions of a large portion of the gene products remain insufficiently characterized. In addition, the “in silico” prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or function genomics approaches. Results We propose a novel approach, Information Theory-based Semantic Similarity (ITSS), to automatically predict molecular functions of genes based on Gene Ontology annotations. We have demonstrated using a 10-fold cross-validation that the ITSS algorithm obtains prediction accuracies (Precision 97%, Recall 77%) comparable to other machine learning algorithms when applied to similarly dense annotated portions of the GO datasets. In addition, such method can generate highly accurate predictions in sparsely annotated portions of GO, in which previous algorithm failed to do so. As a result, our technique generates an order of magnitude more gene function predictions than previous methods. Further, this paper presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions for an evaluation than generally used cross-validations type of evaluations. By manually assessing a random sample of 100 predictions conducted in a historical roll-back evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43%–58%) can be achieved for the human GO Annotation file dated 2003. Availability The program is available on request. The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset are available at http://phenos.bsd.uchicago.edu/mphenogo/prediction_result_2005.txt. PMID:17646340

  7. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models

    Directory of Open Access Journals (Sweden)

    Smith Terry J

    2004-03-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

  8. From Ecological Sounding Artifacts Towards Sonic Artifact Ecologies

    DEFF Research Database (Denmark)

    Erkut, Cumhur; Serafin, Stefania

    2016-01-01

    The discipline of sonic interaction design has been focused on the interaction between a single user and an artifact. This strongly limits one of the fundamental aspects of music as a social and interactive experience. In this paper we propose sonic artifact ecologies as a mean to examine...

  9. Spherical artifacts on ferrograms

    Science.gov (United States)

    Jones, W. R., Jr.

    1976-01-01

    In the past, hollow spheres detected on ferrograms have been interpreted as being due to fretting, abrasion, cavitation erosion, and fatigue-related processes. Here it is reported that such spheres were found to result from the fact that a routine grinding operation on a steel plate was carried out about 20 feet away from the ferrograph. A similar grinding operation was performed on a piece of low carbon steel a few feet from the ferrograph, and after a few minutes of grinding, the resulting ferrogram contained thousands of particles of which more than 90% were spherical. Because of the widespread occurrence of ordinary grinding operations, it seems prudent that those utilizing the ferrograph be cognizant of this type of artifact.

  10. Artifacts in digital images

    Science.gov (United States)

    Lorre, J. J.; Gillespie, A. R.

    1980-01-01

    Three kinds of artifacts unique to digital images are illustrated, namely aliasing caused by undersampling, interference phenomena caused by improper display of images, and harmonic overtones caused by quantization of amplitudes. Special attention is given to undersampling when the sample size and interval are the same. It is noted that this situation is important because it is typical of solid-state cameras. Quantization of image data of necessity introduces energy at harmonic overtones of the image spectrum. This energy is aliased if the frequency of the overtones is greater than 0.5 cycle/pixel. It cannot be selectively removed from the image through filtering, and the best way to suppress it is to maximize the amplification of the sensor before digital encoding.

  11. Archaeology, Artifacts, and Cosmochemistry

    Science.gov (United States)

    Martel, L. M. V.

    2017-06-01

    PSRD covers research that ascertains the content, formation, and evolution of our Solar System and planetary systems in general. Our archives are full of sample-based studies of extraterrestrial materials that relate to the building of planets, moons, and minor bodies. Rarely do we cover the cosmochemistry of artifacts, but the importance of cosmochemistry is abundantly clear in this story of artisan iron beads of archaeological significance and the quest to find the source meteorite. Twenty-two meteoritic iron beads, recovered from mounds in Havana, Illinois of the Hopewell people and culture, have been identified as pieces of the Anoka iron meteorite, according to work by Timothy McCoy (National Museum of Natural History, Smithsonian Institution), Amy Marquardt (undergraduate intern at the NMNH/SI and now at the University of Colorado at Boulder), John Wasson (UCLA), Richard Ash (University of Maryland), and Edward Vicenzi (SI).

  12. Sound as artifact

    Science.gov (United States)

    Benjamin, Jeffrey L.

    A distinguishing feature of the discipline of archaeology is its reliance upon sensory dependant investigation. As perceived by all of the senses, the felt environment is a unique area of archaeological knowledge. It is generally accepted that the emergence of industrial processes in the recent past has been accompanied by unprecedented sonic extremes. The work of environmental historians has provided ample evidence that the introduction of much of this unwanted sound, or "noise" was an area of contestation. More recent research in the history of sound has called for more nuanced distinctions than the noisy/quiet dichotomy. Acoustic archaeology tends to focus upon a reconstruction of sound producing instruments and spaces with a primary goal of ascertaining intentionality. Most archaeoacoustic research is focused on learning more about the sonic world of people within prehistoric timeframes while some research has been done on historic sites. In this thesis, by way of a meditation on industrial sound and the physical remains of the Quincy Mining Company blacksmith shop (Hancock, MI) in particular, I argue for an acceptance and inclusion of sound as artifact in and of itself. I am introducing the concept of an individual sound-form, or sonifact , as a reproducible, repeatable, representable physical entity, created by tangible, perhaps even visible, host-artifacts. A sonifact is a sound that endures through time, with negligible variability. Through the piecing together of historical and archaeological evidence, in this thesis I present a plausible sonifactual assemblage at the blacksmith shop in April 1916 as it may have been experienced by an individual traversing the vicinity on foot: an 'historic soundwalk.' The sensory apprehension of abandoned industrial sites is multi-faceted. In this thesis I hope to make the case for an acceptance of sound as a primary heritage value when thinking about the industrial past, and also for an increased awareness and acceptance

  13. Prediction of human protein function according to Gene Ontology categories

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...... can all be predicted. Although the method relies on protein sequences as the sole input, it does not rely on sequence similarity, but instead on sequence derived protein features such as predicted post translational modifications (PTMs), protein sorting signals and physical/chemical properties...

  14. Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG.

    Science.gov (United States)

    Li, Zhen; Li, Bi-Qing; Jiang, Min; Chen, Lei; Zhang, Jian; Liu, Lin; Huang, Tao

    2013-01-01

    One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

  15. Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG

    Directory of Open Access Journals (Sweden)

    Zhen Li

    2013-01-01

    Full Text Available One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR method followed by incremental feature selection (IFS. 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

  16. Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.

    Directory of Open Access Journals (Sweden)

    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.

  17. 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.

  18. Predictive screening for regulators of conserved functional gene modules (gene batteries in mammals

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    Sigvardsson Mikael

    2005-05-01

    Full Text Available Abstract Background The expression of gene batteries, genomic units of functionally linked genes which are activated by similar sets of cis- and trans-acting regulators, has been proposed as a major determinant of cell specialization in metazoans. We developed a predictive procedure to screen the mouse and human genomes and transcriptomes for cases of gene-battery-like regulation. Results In a screen that covered ~40 per cent of all annotated protein-coding genes, we identified 21 co-expressed gene clusters with statistically supported sharing of cis-regulatory sequence elements. 66 predicted cases of over-represented transcription factor binding motifs were validated against the literature and fell into three categories: (i previously described cases of gene battery-like regulation, (ii previously unreported cases of gene battery-like regulation with some support in a limited number of genes, and (iii predicted cases that currently lack experimental support. The novel predictions include for example Sox 17 and RFX transcription factor binding sites that were detected in ~10% of all testis specific genes, and HNF-1 and 4 binding sites that were detected in ~30% of all kidney specific genes respectively. The results are publicly available at http://www.wlab.gu.se/lindahl/genebatteries. Conclusion 21 co-expressed gene clusters were enriched for a total of 66 shared cis-regulatory sequence elements. A majority of these predictions represent novel cases of potential co-regulation of functionally coupled proteins. Critical technical parameters were evaluated, and the results and the methods provide a valuable resource for future experimental design.

  19. Prediction of human protein function according to Gene Ontology categories

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

  20. Camera artifacts in IUE spectra

    Science.gov (United States)

    Bruegman, O. W.; Crenshaw, D. M.

    1994-01-01

    This study of emission line mimicking features in the IUE cameras has produced an atlas of artifiacts in high-dispersion images with an accompanying table of prominent artifacts and a table of prominent artifacts in the raw images along with a medium image of the sky background for each IUE camera.

  1. Artifacts in musculoskeletal MR imaging.

    Science.gov (United States)

    Singh, Dinesh R; Chin, Michael S M; Peh, Wilfred C G

    2014-02-01

    MR imaging has become an important diagnostic tool in the evaluation of a vast number of pathologies and is of foremost importance in the evaluation of spine, joints, and soft tissue structures of the musculoskeletal system. MR imaging is susceptible to various artifacts that may affect the image quality or even simulate pathologies. Some of these artifacts have gained special importance with the use of higher field strength magnets and with the increasing need for MR imaging in postoperative patients, especially those with previous joint replacements or metallic implants. Artifacts may arise from patient motion or could be due to periodic motion, such as vascular and cardiac pulsation. Artifacts could also arise from various protocol errors including saturation, wraparound, truncation, shading, partial volume averaging, and radiofrequency interference artifacts. Susceptibility artifact occurs at interfaces with different magnetic susceptibilities and is of special importance with increasing use of metallic joint replacement prostheses. Magic angle phenomenon is a special type of artifact that occurs in musculoskeletal MR imaging. It is essential to recognize these artifacts and to correct them because they may produce pitfalls in image interpretation.

  2. Teaching World Cultures through Artifacts

    Science.gov (United States)

    Hauf, James E.

    2010-01-01

    Teaching world cultures in the middle-level geography classroom presents challenges both because of the complexity of culture and because of the characteristics of students of this age. One effective way to teach about a culture is through the use of cultural artifacts. This article discusses how to collect and use cultural artifacts in the…

  3. Intention, History, and Artifact Concepts.

    Science.gov (United States)

    Bloom, Paul

    1996-01-01

    Claims that people determine whether something is a member of a given artifact kind by inferring that it was successfully created with the intention that it belong to that kind. Discusses function-based and intentional-historical accounts of artifact concepts. Concludes that a rich set of inferential capacities is needed to constitute a theory of…

  4. Prediction of epigenetically regulated genes in breast cancer cell lines

    Energy Technology Data Exchange (ETDEWEB)

    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.

  5. Prediction of epigenetically regulated genes in breast cancer cell lines

    Directory of Open Access Journals (Sweden)

    Lu Yontao

    2010-06-01

    methylation profles and gene expression in the 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. Conclusions 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.

  6. 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

  7. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Science.gov (United States)

    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

  8. Ion channel gene expression predicts survival in glioma patients.

    Science.gov (United States)

    Wang, Rong; Gurguis, Christopher I; Gu, Wanjun; Ko, Eun A; Lim, Inja; Bang, Hyoweon; Zhou, Tong; Ko, Jae-Hong

    2015-08-03

    Ion channels are important regulators in cell proliferation, migration, and apoptosis. The malfunction and/or aberrant expression of ion channels may disrupt these important biological processes and influence cancer progression. In this study, we investigate the expression pattern of ion channel genes in glioma. We designate 18 ion channel genes that are differentially expressed in high-grade glioma as a prognostic molecular signature. This ion channel gene expression based signature predicts glioma outcome in three independent validation cohorts. Interestingly, 16 of these 18 genes were down-regulated in high-grade glioma. This signature is independent of traditional clinical, molecular, and histological factors. Resampling tests indicate that the prognostic power of the signature outperforms random gene sets selected from human genome in all the validation cohorts. More importantly, this signature performs better than the random gene signatures selected from glioma-associated genes in two out of three validation datasets. This study implicates ion channels in brain cancer, thus expanding on knowledge of their roles in other cancers. Individualized profiling of ion channel gene expression serves as a superior and independent prognostic tool for glioma patients.

  9. The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

    Science.gov (United States)

    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.

  10. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.

    Science.gov (United States)

    Chen, Lei; Zhang, Yu-Hang; Wang, ShaoPeng; Zhang, YunHua; Huang, Tao; Cai, Yu-Dong

    2017-01-01

    Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

  11. Turquoise Artifact from Teotihuacan

    Energy Technology Data Exchange (ETDEWEB)

    Spence, Michael W.; Harbottle, Garman; Weigand, Phil C.

    1999-07-01

    Turquoise artifacts appeared sporadically in Mesoamerica as early as the Formative period (Merry de Morales 1987:100, Figure 8.4; Weigand 1989:43). Most occurrences, however, postdate the collapse of Teotihuacan. In the Late Classic and Postclassic periods increasing quantities are found, often in the form of elaborate mosaics, in a wide variety of contexts in central, west and northwest Mexico. Neutron activation analysis has determined that much of this turquoise derives from sources in the southwestern United States (Weigand et al. 1977; Harbottle and Weigand 1992; Weigand and Harbottle 1993). Teotihuacan played a major role in Mesoamerica during the Terminal Formative and Early-Middle Classic periods. It was the dominant power in central Mexico from about the time of Christ to its collapse at about A.D. 650 (Millon 1988, 1992; Cowgill 1996). Throughout this period goods flowed into Teotihuacan from many parts of the Mesoamerican world. Despite this widespread economic interaction, only two pieces of turquoise have been recovered in the city. In the following pages, the context and implications of one of these finds will be examined.

  12. Evolving DNA motifs to predict GeneChip probe performance

    Directory of Open Access Journals (Sweden)

    Harrison AP

    2009-03-01

    Full Text Available Abstract Background Affymetrix High Density Oligonuclotide Arrays (HDONA simultaneously measure expression of thousands of genes using millions of probes. We use correlations between measurements for the same gene across 6685 human tissue samples from NCBI's GEO database to indicated the quality of individual HG-U133A probes. Low correlation indicates a poor probe. Results Regular expressions can be automatically created from a Backus-Naur form (BNF context-free grammar using strongly typed genetic programming. Conclusion The automatically produced motif is better at predicting poor DNA sequences than an existing human generated RE, suggesting runs of Cytosine and Guanine and mixtures should all be avoided.

  13. Investigating media artifacts with children

    DEFF Research Database (Denmark)

    Chimirri, Niklas Alexander

    The dissertation’s aim is to explore the everyday relevance media artifacts have for young children. It discusses and further develops analytical concepts that are committed to taking the children’s perspectives on possibilities and limitations of such artifacts seriously. These conceptual...... developments are rooted in the author’s participation in a daycare practice in Berlin, Germany. The daycare’s situational approach precisely attempted to draw on the children’s everyday life experiences so as to engage in problem-oriented learning projects, on media artifacts and beyond....

  14. Visual quality beyond artifact visibility

    Science.gov (United States)

    Redi, Judith A.

    2013-03-01

    The Electronic imaging community has devoted a lot of effort to the development of technologies that can predict the visual quality of images and videos, as a basis for the delivery of optimal visual quality to the user. These systems have been based for the most part on a visibility-centric approach, assuming the more artifacts are visible, the higher is the annoyance they provoke, the lower the visual quality. Despite the remarkable results achieved with this approach, recently a number of studies suggested that the visibility-centric approach to visual quality might have limitations, and that other factors might influence the overall quality impression of an image or video, depending on cognitive and affective mechanisms that work on top of perception. In particular, interest in the visual content, engagement and context of usage have been found to impact on the overall quality impression of the image/video. In this paper, we review these studies and explore the impact that affective and cognitive processes have on the visual quality. In addition, as a case study, we present the results of an experiment investigating on the impact of aesthetic appeal on visual quality, and we show that users tend to be more demanding in terms of visual quality judging beautiful images.

  15. An observer model for quantifying panning artifacts in digital pathology

    Science.gov (United States)

    Avanaki, Ali R. N.; Espig, Kathryn S.; Xthona, Albert; Lanciault, Christian; Kimpe, Tom R. L.

    2017-03-01

    Typically, pathologists pan from one region of a slide to another, choosing areas of interest for closer inspection. Due to finite frame rate and imperfect zero-order hold reconstruction (i.e., the non-zero time to reach the target brightness after a change in pixel drive), panning in whole slide images (WSI) cause visual artifacts. It is important to study the impact of such artifacts since research suggests that 49% of navigation is conducted in low-power/overview with digital pathology (Molin et al., Histopathology 2015). In this paper, we explain what types of medical information may be harmed by panning artifacts, propose a method to simulate panning artifacts, and design an observer model to predict the impact of panning artifacts on typical human observers' performance in basic diagnostically relevant visual tasks. The proposed observer model is based on derivation of perceived object border maps from luminance and chrominance information and may be tuned to account for visual acuity of the human observer to be modeled. Our results suggest that increasing the contrast (e.g., using a wide gamut display) with a slow response panel may not mitigate the panning artifacts which mostly affect visual tasks involving spatial discrimination of objects (e.g., normal vs abnormal structure, cell type and spatial relationships between them, and low-power nuclear morphology), and that the panning artifacts worsen with increasing panning speed. The proposed methods may be used as building blocks in an automatic WSI quality assessment framework.

  16. Cytological artifacts masquerading interpretation

    Science.gov (United States)

    Sahay, Khushboo; Mehendiratta, Monica; Rehani, Shweta; Kumra, Madhumani; Sharma, Rashi; Kardam, Priyanka

    2013-01-01

    Background: Cytological artifacts are important to learn because an error in routine laboratory practice can bring out an erroneous result. Aims: The aim of this study was to analyze the effects of delayed fixation and morphological discrepancies created by deliberate addition of extraneous factors on the interpretation and/or diagnosis of an oral cytosmear. Materials and Methods: A prospective study was carried out using papanicolaou and hematoxylin and eosin-stained oral smears, 6 each from 66 volunteer dental students with deliberate variation in fixation delay timings, with and without changes in temperature, undue pressure while smear making and intentional addition of contaminants. The fixation delay at room temperature was carried out at an interval of every 30 minutes, 1 day and 1 week and was continued till the end of 1 day, 1 week, and 1 month, respectively. The temperature variations included 60 to 70°C and 3 to 4°C. Results: Light microscopically, the effect of delayed fixation at room temperature appeared first on cytoplasm followed by nucleus within the first 2 hours and on the 4th day, respectively, till complete cytoplasmic degeneration on the 23rd day. However, delayed fixation at variable temperature brought faster degenerative changes at higher temperature than lower temperature. Effect of extraneous factors revealed some interesting facts. Conclusions: In order to justify a cytosmear interpretation, a cytologist must be well acquainted with delayed fixation-induced cellular changes and microscopic appearances of common contaminants so as to implicate better prognosis and therapy. PMID:24648667

  17. Identification of rat genes by TWINSCAN gene prediction, RT-PCR, and direct sequencing

    DEFF Research Database (Denmark)

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

    2004-01-01

    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...... 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...

  18. Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

    Science.gov (United States)

    Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

  19. Dinucleotide controlled null models for comparative RNA gene prediction

    Directory of Open Access Journals (Sweden)

    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

  20. Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions.

    Science.gov (United States)

    Kuppuswamy, Usha; Ananthasubramanian, Seshan; Wang, Yanli; Balakrishnan, Narayanaswamy; Ganapathiraju, Madhavi K

    2014-04-03

    The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably. This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in

  1. Improving metabolic flux predictions using absolute gene expression data

    Directory of Open Access Journals (Sweden)

    Lee Dave

    2012-06-01

    Full Text Available Abstract Background Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se. Results An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production. Conclusion Due to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method’s ability to generate condition- and tissue-specific flux predictions in multicellular organisms.

  2. Partial AUC maximization for essential gene prediction using genetic algorithms.

    Science.gov (United States)

    Hwang, Kyu-Baek; Ha, Beom-Yong; Ju, Sanghun; Kim, Sangsoo

    2013-01-01

    Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

  3. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    Science.gov (United States)

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  4. Data mining approach to predict BRCA1 gene mutation

    Directory of Open Access Journals (Sweden)

    Olegas Niakšu

    2013-09-01

    Full Text Available Breast cancer is the most frequent women cancer form and one of the leading mortality causes among women around the world. Patients with pathological mutation of a BRCA gene have 65% lifelong breast cancer probability. It is known that such patients have different cause of illness. In this study, we have proposed a new approach for the prediction of BRCA mutation carriers by methodically applying knowledge discovery steps and utilizing data mining methods. An alternative BRCA risk assessment model has been created utilizing decision tree classifier model. The biggest challenge was a very small size and imbalanced nature of the initial dataset, which have been collected by clinicians during 4 years of clinical trial. Iterative optimization of initial dataset, optimal algorithms selection and their parameterization have resulted in higher classifier model performance, with acceptable prediction accuracy for the clinical usage. In this study, three data mining problems have been analyzed using eleven data mining algorithms.

  5. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes

    Science.gov (United States)

    Gerstung, Moritz; Pellagatti, Andrea; Malcovati, Luca; Giagounidis, Aristoteles; Porta, Matteo G Della; Jädersten, Martin; Dolatshad, Hamid; Verma, Amit; Cross, Nicholas C. P.; Vyas, Paresh; Killick, Sally; Hellström-Lindberg, Eva; Cazzola, Mario; Papaemmanuil, Elli; Campbell, Peter J.; Boultwood, Jacqueline

    2015-01-01

    Cancer is a genetic disease, but two patients rarely have identical genotypes. Similarly, patients differ in their clinicopathological parameters, but how genotypic and phenotypic heterogeneity are interconnected is not well understood. Here we build statistical models to disentangle the effect of 12 recurrently mutated genes and 4 cytogenetic alterations on gene expression, diagnostic clinical variables and outcome in 124 patients with myelodysplastic syndromes. Overall, one or more genetic lesions correlate with expression levels of ~20% of all genes, explaining 20–65% of observed expression variability. Differential expression patterns vary between mutations and reflect the underlying biology, such as aberrant polycomb repression for ASXL1 and EZH2 mutations or perturbed gene dosage for copy-number changes. In predicting survival, genomic, transcriptomic and diagnostic clinical variables all have utility, with the largest contribution from the transcriptome. Similar observations are made on the TCGA acute myeloid leukaemia cohort, confirming the general trends reported here. PMID:25574665

  6. A methodology for validating artifact removal techniques for physiological signals.

    Science.gov (United States)

    Sweeney, Kevin T; Ayaz, Hasan; Ward, Tomás E; Izzetoglu, Meltem; McLoone, Seán F; Onaral, Banu

    2012-09-01

    Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a "ground truth" signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this "ground truth," together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

  7. Evaluation of Audio Compression Artifacts

    Directory of Open Access Journals (Sweden)

    M. Herrera Martinez

    2007-01-01

    Full Text Available This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal and the algorithm of the audio-coding system, different types of audible errors arise. These errors are called coding artifacts. Although three kinds of artifacts are perceivable in the auditory domain, the author proposes that in the coding domain there is only one common cause for the appearance of the artifact, inefficient tracking of transient-stochastic signals. For this purpose, state-of-the art audio coding systems use a wide range of signal processing techniques, including application of the wavelet transform, which is described here. 

  8. Sonographic twinkling artifact for renal calculus detection: correlation with CT.

    Science.gov (United States)

    Dillman, Jonathan R; Kappil, Mariam; Weadock, William J; Rubin, Jonathan M; Platt, Joel F; DiPietro, Michael A; Bude, Ronald O

    2011-06-01

    To retrospectively correlate sonographic color Doppler twinkling artifact within the kidneys with unenhanced computed tomography (CT) in the detection of nephrolithiasis. Institutional review board approval was obtained for this retrospective HIPAA-complaint investigation, and the informed consent requirement was waived. Sonographic imaging reports describing the presence of renal twinkling artifact between January 2008 and September 2009 were identified. Subjects who did not undergo unenhanced abdominal CT within 2 weeks after sonography were excluded. Ultrasound examinations were reviewed by three radiologists working together, and presence, number, location, and size of renal twinkling artifacts were documented by consensus opinion. Sonographic findings were correlated with unenhanced CT (5-mm section width, no overlap) for nephrolithiasis and other causes of twinkling artifact. The number, location, and size of renal calculi at CT were documented. The presence of sonographic renal twinkling artifact, in general, had a 78% (95% confidence interval: 0.66, 0.90) positive predictive value for nephrolithiasis anywhere in the kidneys at CT. The true-positive rate of twinkling artifact for confirmed calculi at CT was 49% (73 of 148 twinkling foci), while the false-positive rate was 51% (75 of 148 twinkling foci). The overall sensitivity of twinkling artifact for the detection of specific individual renal calculi observed at CT was 55% (95% confidence interval: 0.47, 0.64). While renal twinkling artifact is commonly associated with nephrolithiasis, this finding is relatively insensitive in routine clinical practice and has a high false-positive rate when 5-mm unenhanced CT images are used as the reference standard. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11102128/-/DC1. RSNA, 2011

  9. Specific regulatory motifs predict glucocorticoid responsiveness of hippocampal gene expression.

    Science.gov (United States)

    Datson, N A; Polman, J A E; de Jonge, R T; van Boheemen, P T M; van Maanen, E M T; Welten, J; McEwen, B S; Meiland, H C; Meijer, O C

    2011-10-01

    The glucocorticoid receptor (GR) is an ubiquitously expressed ligand-activated transcription factor that mediates effects of cortisol in relation to adaptation to stress. In the brain, GR affects the hippocampus to modulate memory processes through direct binding to glucocorticoid response elements (GREs) in the DNA. However, its effects are to a high degree cell specific, and its target genes in different cell types as well as the mechanisms conferring this specificity are largely unknown. To gain insight in hippocampal GR signaling, we characterized to which GRE GR binds in the rat hippocampus. Using a position-specific scoring matrix, we identified evolutionary-conserved putative GREs from a microarray based set of hippocampal target genes. Using chromatin immunoprecipitation, we were able to confirm GR binding to 15 out of a selection of 32 predicted sites (47%). The majority of these 15 GREs are previously undescribed and thus represent novel GREs that bind GR and therefore may be functional in the rat hippocampus. GRE nucleotide composition was not predictive for binding of GR to a GRE. A search for conserved flanking sequences that may predict GR-GRE interaction resulted in the identification of GC-box associated motifs, such as Myc-associated zinc finger protein 1, within 2 kb of GREs with GR binding in the hippocampus. This enrichment was not present around nonbinding GRE sequences nor around proven GR-binding sites from a mesenchymal stem-like cell dataset that we analyzed. GC-binding transcription factors therefore may be unique partners for DNA-bound GR and may in part explain cell-specific transcriptional regulation by glucocorticoids in the context of the hippocampus.

  10. Pim-1 kinase inhibits the activation of reporter gene expression in Elk-1 and c-Fos reporting systems but not the endogenous gene expression: an artifact of the reporter gene assay by transient co-transfection

    Directory of Open Access Journals (Sweden)

    Yan B.

    2006-01-01

    Full Text Available We have studied the molecular mechanism and signal transduction of pim-1, an oncogene encoding a serine-threonine kinase. This is a true oncogene which prolongs survival and inhibits apoptosis of hematopoietic cells. In order to determine whether the effects of Pim-1 occur by regulation of the mitogen-activated protein kinase pathway, we used a transcriptional reporter assay by transient co-transfection as a screening method. In this study, we found that Pim-1 inhibited the Elk-1 and NFkappaB transcriptional activities induced by activation of the mitogen-activated protein kinase cascade in reporter gene assays. However, Western blots showed that the induction of Elk-1-regulated expression of endogenous c-Fos was not affected by Pim-1. The phosphorylation and activation of neither Erk1/2 nor Elk-1 was influenced by Pim-1. Also, in the gel shift assay, the pattern of endogenous NFkappaB binding to its probe was not changed in any manner by Pim-1. These data indicate that Pim-1 does not regulate the activation of Erk1/2, Elk-1 or NFkappaB. These contrasting results suggest a pitfall of the transient co-transfection reporter assay in analyzing the regulation of transcription factors outside of the chromosome context. It ensures that results from reporter gene expression assay should be verified by study of endogenous gene expression.

  11. TH-C-BRD-06: A Novel MRI Based CT Artifact Correction Method for Improving Proton Range Calculation in the Presence of Severe CT Artifacts

    Energy Technology Data Exchange (ETDEWEB)

    Park, P; Schreibmann, E; Fox, T; Roper, J; Elder, E; Tejani, M; Crocker, I; Curran, W; Dhabaan, A [Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA (United States)

    2014-06-15

    Purpose: Severe CT artifacts can impair our ability to accurately calculate proton range thereby resulting in a clinically unacceptable treatment plan. In this work, we investigated a novel CT artifact correction method based on a coregistered MRI and investigated its ability to estimate CT HU and proton range in the presence of severe CT artifacts. Methods: The proposed method corrects corrupted CT data using a coregistered MRI to guide the mapping of CT values from a nearby artifact-free region. First patient MRI and CT images were registered using 3D deformable image registration software based on B-spline and mutual information. The CT slice with severe artifacts was selected as well as a nearby slice free of artifacts (e.g. 1cm away from the artifact). The two sets of paired MRI and CT images at different slice locations were further registered by applying 2D deformable image registration. Based on the artifact free paired MRI and CT images, a comprehensive geospatial analysis was performed to predict the correct CT HU of the CT image with severe artifact. For a proof of concept, a known artifact was introduced that changed the ground truth CT HU value up to 30% and up to 5cm error in proton range. The ability of the proposed method to recover the ground truth was quantified using a selected head and neck case. Results: A significant improvement in image quality was observed visually. Our proof of concept study showed that 90% of area that had 30% errors in CT HU was corrected to 3% of its ground truth value. Furthermore, the maximum proton range error up to 5cm was reduced to 4mm error. Conclusion: MRI based CT artifact correction method can improve CT image quality and proton range calculation for patients with severe CT artifacts.

  12. Text Signals Influence Team Artifacts

    Science.gov (United States)

    Clariana, Roy B.; Rysavy, Monica D.; Taricani, Ellen

    2015-01-01

    This exploratory quasi-experimental investigation describes the influence of text signals on team visual map artifacts. In two course sections, four-member teams were given one of two print-based text passage versions on the course-related topic "Social influence in groups" downloaded from Wikipedia; this text had two paragraphs, each…

  13. Toddlers View Artifact Function Normatively

    Science.gov (United States)

    Casler, Krista; Terziyan, Treysi; Greene, Kimberly

    2009-01-01

    When children use objects like adults, are they simply tracking regularities in others' object use, or are they demonstrating a normatively defined awareness that there are right and wrong ways to act? This study provides the first evidence for the latter possibility. Young 2- and 3-year-olds (n = 32) learned functions of 6 artifacts, both…

  14. Technical artifacts: An integrated perspective

    NARCIS (Netherlands)

    Borgo, S.; Franssen, M.P.M.; Garbacz, P.; Kitamura, Y.; Mizoguchi, R.; Vermaas, P.E.

    2014-01-01

    Humans are always interested in distinguishing natural and artificial entities although there is no sharp demarcation between the two categories. Surprisingly, things do not improve when the second type of entities is restricted to the arguably more constrained realm of physical technical artifacts.

  15. Modeling Software Processes and Artifacts

    NARCIS (Netherlands)

    van den Berg, Klaas; Bosch, Jan; Mitchell, Stuart

    1997-01-01

    The workshop on Modeling Software Processes and Artifacts explored the application of object technology in process modeling. After the introduction and the invited lecture, a number of participants presented their position papers. First, an overview is given on some background work, and the aims, as

  16. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    Directory of Open Access Journals (Sweden)

    Gao Haichun

    2007-08-01

    Full Text Available Abstract Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT, which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under

  17. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks.

    Science.gov (United States)

    Xiang, Zuoshuang; Qin, Tingting; Qin, Zhaohui S; He, Yongqun

    2013-10-16

    The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining

  18. Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays

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    Matsui Shigeyuki

    2006-03-01

    Full Text Available Abstract Background Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression microarray technology has allowed the selection of relevant marker genes from a large pool of candidate genes in early-phased, developmental prognostic marker studies. The primary analytical task in such studies is to select a small fraction of relevant genes, typically from a list of significant genes, for further investigation in subsequent studies. Results We develop a methodology for predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays. Key components in this methodology include building prediction models, assessing predictive performance of prediction models, and assessing significance of prediction results. As particular specifications, we assume Cox proportional hazard models with a compound covariate. For assessing predictive accuracy, we propose to use the cross-validated log partial likelihood. To assess significance of prediction results, we apply permutation procedures in cross-validated prediction. As an additional key component peculiar to prognostic prediction, we also consider incorporation of standard prognostic factors. The methodology is evaluated using both simulated and real data. Conclusion The developed methodology for prognostic prediction using a subset of significant genes can provide new insights based on predictive capability, possibly incorporating standard prognostic factors, in selecting a fraction of relevant genes for subsequent studies.

  19. Single authentication: exposing weighted splining artifacts

    Science.gov (United States)

    Ciptasari, Rimba W.

    2016-05-01

    A common form of manipulation is to combine parts of the image fragment into another different image either to remove or blend the objects. Inspired by this situation, we propose a single authentication technique for detecting traces of weighted average splining technique. In this paper, we assume that image composite could be created by joining two images so that the edge between them is imperceptible. The weighted average technique is constructed from overlapped images so that it is possible to compute the gray level value of points within a transition zone. This approach works on the assumption that although splining process leaves the transition zone smoothly. They may, nevertheless, alter the underlying statistics of an image. In other words, it introduces specific correlation into the image. The proposed idea dealing with identifying these correlations is to generate an original model of both weighting function, left and right functions, as references to their synthetic models. The overall process of the authentication is divided into two main stages, which are pixel predictive coding and weighting function estimation. In the former stage, the set of intensity pairs {Il,Ir} is computed by exploiting pixel extrapolation technique. The least-squares estimation method is then employed to yield the weighted coefficients. We show the efficacy of the proposed scheme on revealing the splining artifacts. We believe that this is the first work that exposes the image splining artifact as evidence of digital tampering.

  20. Withanolide artifacts formed in methanol.

    Science.gov (United States)

    Cao, Cong-Mei; Zhang, Huaping; Gallagher, Robert J; Timmermann, Barbara N

    2013-11-22

    Methanol solutions of the main withanolides (6-8) naturally present in Physalis longifolia yielded five artificial withanolides (1-5), including three new compounds (1-3). Withanolides 1 and 2 were identified as intramolecular Michael addition derivatives, while withanolides 3-5 were the result of intermolecular Michael addition. A comprehensive literature investigation was conducted to identify potential withanolide Michael addition artifacts isolated from Solanaceous species to date.

  1. WeGET: predicting new genes for molecular systems by weighted co-expression

    NARCIS (Netherlands)

    Szklarczyk, R.; Megchelenbrink, W.; Cizek, P.; Ledent, M.; Velemans, G.; Szklarczyk, D.; Huynen, M.A.

    2016-01-01

    We have developed the Weighted Gene Expression Tool and database (WeGET, http://weget.cmbi.umcn.nl) for the prediction of new genes of a molecular system by correlated gene expression. WeGET utilizes a compendium of 465 human and 560 murine gene expression datasets that have been collected from

  2. Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

    Directory of Open Access Journals (Sweden)

    Chi Zhang

    2015-01-01

    Full Text Available Electroencephalogram (EEG is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA, wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

  3. Data Mining Strategy for "Gene Prediction" with Special Reference to Cotton Genome

    Institute of Scientific and Technical Information of China (English)

    KSHIRSAGAR Manali; BALASUBRAMANI G; SINGH Col Gurmit

    2008-01-01

    @@ This paper presents an integrated approach towards solving the problem of "Gene Prediction".The "Gene Prediction" problem solving undergoes well defined stages starting with a DNA sequence as input and lab treatment and computational analysis go hands in hands throughout the process.Many bioinformatics tools are available for analysis at different stages of "Gene Prediction",but a simplified and integrated approach is needed to support and speed up the task of a life scientist.

  4. Combinations of gene ontology and pathway characterize and predict prognosis genes for recurrence of gastric cancer after surgery.

    Science.gov (United States)

    Fan, Haiyan; Guo, Zhanjun; Wang, Cuijv

    2015-09-01

    Gastric cancer (GC) is the second leading cause of death from cancer globally. The most common cause of GC is the infection of Helicobacter pylori, but ∼11% of cases are caused by genetic factors. However, recurrences occur in approximately one-third of stage II GC patients, even if they are treated with adjuvant chemotherapy or chemoradiotherapy. This is potentially due to expression variation of genes; some candidate prognostic genes were identified in patients with high-risk recurrences. The objective of this study was to develop an effective computational method for meaningfully interpreting these GC-related genes and accurately predicting novel prognostic genes for high-risk recurrence patients. We employed properties of genes (gene ontology [GO] and KEGG pathway information) as features to characterize GC-related genes. We obtained an optimal set of features for interpreting these genes. By applying the minimum redundancy maximum relevance algorithm, we predicted the GC-related genes. With the same approach, we further predicted the genes for the prognostic of high-risk recurrence. We obtained 1104 GO terms and KEGG pathways and 530 GO terms and KEGG pathways, respectively, that characterized GC-related genes and recurrence-related genes well. Finally, three novel prognostic genes were predicted to help supplement genetic markers of high-risk GC patients for recurrence after surgery. An in-depth text mining indicated that the results are quite consistent with previous knowledge. Survival analysis of patients confirmed the novel prognostic genes as markers. By analyzing the related genes, we developed a systematic method to interpret the possible underlying mechanism of GC. The novel prognostic genes facilitate the understanding and therapy of GC recurrences after surgery.

  5. Artifacts in three-dimensional transesophageal echocardiography.

    Science.gov (United States)

    Faletra, Francesco Fulvio; Ramamurthi, Alamelu; Dequarti, Maria Cristina; Leo, Laura Anna; Moccetti, Tiziano; Pandian, Natesa

    2014-05-01

    Three-dimensional (3D) transesophageal echocardiography (TEE) is subject to the same types of artifacts encountered on two-dimensional TEE. However, when displayed in a 3D format, some of the artifacts appear more "realistic," whereas others are unique to image acquisition and postprocessing. Three-dimensional TEE is increasingly used in the setting of percutaneous catheter-based interventions and ablation procedures, and 3D artifacts caused by the metallic components of catheters and devices are particularly frequent. Knowledge of these artifacts is of paramount relevance to avoid misinterpretation of 3D images. Although artifacts and pitfalls on two-dimensional echocardiography are well described and classified, a systematic description of artifacts in 3D transesophageal echocardiographic images and how they affect 3D imaging is still absent. The aim of this review is to describe the most relevant artifacts on 3D TEE, with particular emphasis on those occurring during percutaneous interventions for structural heart disease and ablation procedures.

  6. Thematic knowledge, artifact concepts, and the left posterior temporal lobe: Where action and object semantics converge.

    Science.gov (United States)

    Kalénine, Solène; Buxbaum, Laurel J

    2016-09-01

    Converging evidence supports the existence of functionally and neuroanatomically distinct taxonomic (similarity-based; e.g., hammer-screwdriver) and thematic (event-based; e.g., hammer-nail) semantic systems. Processing of thematic relations between objects has been shown to selectively recruit the left posterior temporoparietal cortex. Similar posterior regions have also been shown to be critical for knowledge of relationships between actions and manipulable human-made objects (artifacts). Based on the hypothesis that thematic relationships for artifacts rely, at least in part, on action relationships, we assessed the prediction that the same regions of the left posterior temporoparietal cortex would be critical for conceptual processing of artifact-related actions and thematic relations for artifacts. To test this hypothesis, we evaluated processing of taxonomic and thematic relations for artifacts and natural objects as well as artifact action knowledge (gesture recognition) abilities in a large sample of 48 stroke patients with a range of lesion foci in the left hemisphere. Like control participants, patients identified thematic relations faster than taxonomic relations for artifacts, whereas they identified taxonomic relations faster than thematic relations for natural objects. Moreover, response times (RTs) for identifying thematic relations for artifacts selectively predicted performance in gesture recognition. Whole brain Voxel-based Lesion-Symptom Mapping (VLSM) analyses and Region of Interest (ROI) regression analyses further demonstrated that lesions to the left posterior temporal cortex, overlapping with LTO and visual motion area hMT+, were associated both with relatively slower RTs in identifying thematic relations for artifacts and poorer artifact action knowledge in patients. These findings provide novel insights into the functional role of left posterior temporal cortex in thematic knowledge, and suggest that the close association between thematic

  7. Test Data Sets and Evaluation of Gene Prediction Programs on the Rice Genome

    Institute of Scientific and Technical Information of China (English)

    Heng Li; Tao Liu; Hai-Hong Li; Yan Li; Li-Jun Fang; Hui-Min Xie; Wei-Mou Zheng; Bai-Lin Hao; Jin-Song Liu; Zhao Xu; Jiao Jin; Lin Fang; Lei Gao; Yu-Dong Li; Zi-Xing Xing; Shao-Gen Gao

    2005-01-01

    With several rice genome projects approaching completion gene prediction/finding by computer algorithms has become an urgent task. Two test sets were constructed by mapping the newly published 28,469 full-length KOME rice cDNA to the RGP BAC clone sequences of Oryza sativa ssp. japonica: a single-gene set of 550 sequences and a multi-gene set of 62 sequences with 271 genes. These data sets were used to evaluate five ab initio gene prediction programs: RiceHMM,GlimmerR, GeneMark, FGENSH and BGF. The predictions were compared on nucleotide, exon and whole gene structure levels using commonly accepted measures and several new measures. The test results show a progress in performance in chronological order. At the same time complementarity of the programs hints on the possibility of further improvement and on the feasibility of reaching better performance by combining several gene-finders.

  8. Hybrid SPR algorithm to select predictive genes for effectual cancer classification

    OpenAIRE

    2012-01-01

    Designing an automated system for classifying DNA microarray data is an extremely challenging problem because of its high dimension and low amount of sample data. In this paper, a hybrid statistical pattern recognition algorithm is proposed to reduce the dimensionality and select the predictive genes for the classification of cancer. Colon cancer gene expression profiles having 62 samples of 2000 genes were used for the experiment. A gene subset of 6 highly informative genes was selecte...

  9. Advances and perspectives in computational prediction of microbial gene essentiality

    NARCIS (Netherlands)

    Mobegi, Fredrick M; Zomer, Aldert; de Jonge, Marien I; van Hijum, Sacha A F T

    2017-01-01

    The minimal subset of genes required for cellular growth, survival and viability of an organism are classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. This might lead to more efficient antimicrobial drug discovery, to elucidat

  10. Advances and perspectives in computational prediction of microbial gene essentiality

    NARCIS (Netherlands)

    Mobegi, Fredrick M; Zomer, Aldert; de Jonge, Marien I; van Hijum, Sacha A F T

    The minimal subset of genes required for cellular growth, survival and viability of an organism are classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. This might lead to more efficient antimicrobial drug discovery, to

  11. Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize.

    Science.gov (United States)

    Guo, Zhigang; Magwire, Michael M; Basten, Christopher J; Xu, Zhanyou; Wang, Daolong

    2016-12-01

    Predictive ability derived from gene expression and metabolic information was evaluated using genomic prediction methods based on datasets from a public maize panel. With the rapid development of high throughput biological technologies, information from gene expression and metabolites has received growing attention in plant genetics and breeding. In this study, we evaluated the utility of gene expression and metabolic information for genomic prediction using data obtained from a maize diversity panel. Our results show that, when used as predictor variables, gene expression levels and metabolite abundances provided reasonable predictive abilities relative to those based on genetic markers, although these values were not as large as those with genetic markers. Integrating gene expression levels and metabolite abundances with genetic markers significantly improved predictive abilities in comparison to the benchmark genomic best linear unbiased prediction model using genome-wide markers only. Predictive abilities based on gene expression and metabolites were trait-specific and were affected by the time of measurement and tissue samples as well as the number of genes and metabolites included in the model. In general, our results suggest that, rather than being conventionally used as intermediate phenotypes, gene expression and metabolic information can be used as predictors for genomic prediction and help improve genetic gains for complex traits in breeding programs.

  12. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Yun-Xia Zhang

    2016-01-01

    Full Text Available Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes and differentially expressed genes (DEGs between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

  13. WebAUGUSTUS--a web service for training AUGUSTUS and predicting genes in eukaryotes.

    Science.gov (United States)

    Hoff, Katharina J; Stanke, Mario

    2013-07-01

    The prediction of protein coding genes is an important step in the annotation of newly sequenced and assembled genomes. AUGUSTUS is one of the most accurate tools for eukaryotic gene prediction. Here, we present WebAUGUSTUS, a web interface for training AUGUSTUS and predicting genes with AUGUSTUS. Depending on the needs of the user, WebAUGUSTUS generates training gene structures automatically. Besides a genome file, either a file with expressed sequence tags or a file with protein sequences is required for this step. Alternatively, it is possible to submit an externally generated training gene structure file and a genome file. The web service optimizes AUGUSTUS parameters and predicts genes with those parameters. WebAUGUSTUS is available at http://bioinf.uni-greifswald.de/webaugustus.

  14. Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery.

    Science.gov (United States)

    Chen, Yang; Xu, Rong

    2015-01-01

    Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cross-species network to integrate human-human, parasite-parasite and human-parasite protein interactions. Then we extended the random walk algorithm on this network, and used known malaria genes as the seeds to find novel candidate genes for malaria. We validated our algorithms using 77 known malaria genes: 14 human genes and 63 parasite genes were ranked averagely within top 2% and top 4%, respectively among human and parasite genomes. We also evaluated our method for predicting novel malaria genes using a set of 27 genes with literature supporting evidence. Our approach ranked 12 genes within top 1% and 24 genes within top 5%. In addition, we demonstrated that top-ranked candied genes were enriched for drug targets, and identified commonalities underlying top-ranked malaria genes through pathway analysis. In summary, the candidate malaria-associated genes predicted by our data-driven approach have the potential to guide genetics-based anti-malaria drug discovery.

  15. Cleaning MEG artifacts using external cues.

    Science.gov (United States)

    Tal, I; Abeles, M

    2013-07-15

    Using EEG, ECoG, MEG, and microelectrodes to record brain activity is prone to multiple artifacts. The main power line (mains line), video equipment, mechanical vibrations and activities outside the brain are the most common sources of artifacts. MEG amplitudes are low, and even small artifacts distort recordings. In this study, we show how these artifacts can be efficiently removed by recording external cues during MEG recordings. These external cues are subsequently used to register the precise times or spectra of the artifacts. The results indicate that these procedures preserve both the spectra and the time domain wave-shapes of the neuromagnetic signal, while successfully reducing the contribution of the artifacts to the target signals without reducing the rank of the data.

  16. ETS Gene Fusions as Predictive Biomarkers of Resistance to Radiation Therapy for Prostate Cancer

    Science.gov (United States)

    2015-10-01

    Award Number: W81XWH-10-1-0582 TITLE: ETS Gene Fusions as Predictive Biomarkers of Resistance to Radiation Therapy for Prostate Cancer PRINCIPAL...ETS gene fusion status associated with clinical outcomes following radiation therapy , by analyzing both the collected biomarker and clinical data...denotes absence of an ERG fusion). ETS gene fusions status did not predict outcomes following radiation therapy , as demonstrated by Kaplan Meier

  17. Comparison of gene sets for expression profiling: prediction of metastasis from low-malignant breast cancer

    DEFF Research Database (Denmark)

    Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja;

    2007-01-01

    -six tumors from low-risk patients and 34 low-malignant T2 tumors from patients with slightly higher risk have been examined by genome-wide gene expression analysis. Nine prognostic gene sets were tested in this data set. RESULTS: A 32-gene profile (HUMAC32) that accurately predicts metastasis has previously...... sets, mainly developed in high-risk cancers, predict metastasis from low-malignant cancer....

  18. 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...... 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...

  19. Prediction of drug-drug interactions from chemogenomic and gene-gene interactions and analysis of drug-drug interactions

    OpenAIRE

    2013-01-01

    The interactions between multiple drugs administered to an organism concurrently, whether in the form of synergy or antagonism, are of clinical relevance. Moreover, un-derstanding the mechanisms and nature of drug-drug interactions is of great practical and theoretical interest. Work has previously been done on gene-gene and gene-drug interactions, but the prediction and rationalization of drug-drug interactions from this data is not straightforward. We present a strategy for attacking this p...

  20. antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification

    DEFF Research Database (Denmark)

    Blin, Kai; Wolf, Thomas; Chevrette, Marc G.

    2017-01-01

    Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding......, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally...

  1. Prediction of Tumor Outcome Based on Gene Expression Data

    Institute of Scientific and Technical Information of China (English)

    Liu Juan; Hitoshi Iba

    2004-01-01

    Gene expression microarray data can be used to classify tumor types. We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA+WV (Multi-Objective Evolutionary Algorithm+Weighted Voting). MOEA is used to search for a relatively few subsets of informative genes from the high-dimensional gene space, and WV is used as a classification tool. This new method has been applied to predicate the subtypes of lymphoma and outcomes of medulloblastoma. The results are relatively accurate and meaningful compared to those from other methods.

  2. MADR: metal artifact detection and reduction

    Science.gov (United States)

    Jaiswal, Sunil Prasad; Ha, Sungsoo; Mueller, Klaus

    2016-04-01

    Metal in CT-imaged objects drastically reduces the quality of these images due to the severe artifacts it can cause. Most metal artifacts reduction (MAR) algorithms consider the metal-affected sinogram portions as the corrupted data and replace them via sophisticated interpolation methods. While these schemes are successful in removing the metal artifacts, they fail to recover some of the edge information. To address these problems, the frequency shift metal artifact reduction algorithm (FSMAR) was recently proposed. It exploits the information hidden in the uncorrected image and combines the high frequency (edge) components of the uncorrected image with the low frequency components of the corrected image. Although this can effectively transfer the edge information of the uncorrected image, it also introduces some unwanted artifacts. The essential problem of these algorithms is that they lack the capability of detecting the artifacts and as a result cannot discriminate between desired and undesired edges. We propose a scheme that does better in these respects. Our Metal Artifact Detection and Reduction (MADR) scheme constructs a weight map which stores whether a pixel in the uncorrected image belongs to an artifact region or a non-artifact region. This weight matrix is optimal in the Linear Minimum Mean Square Sense (LMMSE). Our results demonstrate that MADR outperforms the existing algorithms and ensures that the anatomical structures close to metal implants are better preserved.

  3. Gene Ontology consistent protein function prediction: the FALCON algorithm applied to six eukaryotic genomes

    NARCIS (Netherlands)

    Kourmpetis, Y.A.I.; Dijk, van A.D.J.; Braak, ter C.J.F.

    2013-01-01

    Gene Ontology (GO) is a hierarchical vocabulary for the description of biological functions and locations, often employed by computational methods for protein function prediction. Due to the structure of GO, function predictions can be self- contradictory. For example, a protein may be predicted to

  4. Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

    OpenAIRE

    Teng Shaolei; Yang Jack Y; Wang Liangjiang

    2013-01-01

    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,...

  5. Large-scale prokaryotic gene prediction and comparison to genome annotation

    DEFF Research Database (Denmark)

    Nielsen, Pernille; Krogh, Anders Stærmose

    2005-01-01

    Motivation: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome...... genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms......-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank. We argue that the average performance of our standardized and fully automated method is slightly better than the annotation....

  6. Improve Survival Prediction Using Principal Components of Gene Expression Data

    Institute of Scientific and Technical Information of China (English)

    Yi-Jing Shen; Shu-Guang Huang

    2006-01-01

    The purpose of many microarray studies is to find the association between gene expression and sample characteristics such as treatment type or sample phenotype.There has been a surge of efforts developing different methods for delineating the association. Aside from the high dimensionality of microarray data, one well recognized challenge is the fact that genes could be complicatedly inter-related, thus making many statistical methods inappropriate to use directly on the expression data. Multivariate methods such as principal component analysis (PCA) and clustering are often used as a part of the effort to capture the gene correlation, and the derived components or clusters are used to describe the association between gene expression and sample phenotype. We propose a method for patient population dichotomization using maximally selected test statistics in combination with the PCA method, which shows favorable results. The proposed method is compared with a currently well-recognized method.

  7. 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...

  8. Evaluating an artifact in design science research

    CSIR Research Space (South Africa)

    Herselman, M

    2015-09-01

    Full Text Available as the artifact which was evaluated. This example from practice can contribute towards an enhanced understanding of the evaluation of DSR artifacts and the contribution of program theory during both the ex ante and ex post part of the development....

  9. Conceptual Model of Artifacts for Design Science Research

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2015-01-01

    We present a conceptual model of design science research artifacts. The model views an artifact at three levels. At the artifact level a selected artifact is viewed as a combination of material and immaterial aspects and a set of representations hereof. At the design level the selected artifact...

  10. Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data.

    Science.gov (United States)

    Teng, Shaolei; Yang, Jack Y; Wang, Liangjiang

    2013-01-01

    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. 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. 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.

  11. Dental material artifacts on MR images.

    Science.gov (United States)

    Hinshaw, D B; Holshouser, B A; Engstrom, H I; Tjan, A H; Christiansen, E L; Catelli, W F

    1988-03-01

    Magnetic resonance (MR) imaging of the head and neck is becoming an important aid in evaluating pathologic conditions of the brain, midface, and pharynx. Certain dental materials cause artifacts during MR imaging of the lower midface. These artifacts can obscure the normal anatomy. This study describes the degree of artifact production caused by various materials commonly used in dental restorations. Of the materials tested, those causing artifacts were made of stainless steel, such as orthodontic bands used for braces, and pins or posts that are commonly drilled into teeth to provide structure or stability before filling. Materials used as temporary or permanent fillings or crowns--such as amalgam, gold alloy, aluminum, microfilled resin, and polyvinyl acrylics--did not cause artifacts in the images.

  12. The Many Faces of Computational Artifacts

    DEFF Research Database (Denmark)

    Christensen, Lars Rune; Harper, Richard

    2016-01-01

    Building on data from fieldwork at a medical department, this paper focuses on the varied nature of computational artifacts in practice. It shows that medical practice relies on multiple heterogeneous computational artifacts that form complex constellations. In the hospital studied...... the computational artifacts are both coordinative, image-generating, and intended for the control of nuclear-physical and chemical processes. Furthermore, the paper entails a critique of the notion of ‘computer support’, for not capturing the diverse constitutive powers of computer technology; its types if you will....... The paper is a step towards establishing a lexicon of computational artifacts in practice. It is a call for a wider effort to systematically conceptualise the multiple and specifiable ways in which computational artifacts may be part of work activities. This is for the benefit of design and our...

  13. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    GAO Lei; LI Xia; GUO Zheng; ZHU MingZhu; LI YanHui; RAO ShaoQi

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to "biology process" by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  14. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  15. A method for quantitative assessment of artifacts in EEG, and an empirical study of artifacts.

    Science.gov (United States)

    Kappel, Simon L; Looney, David; Mandic, Danilo P; Kidmose, Preben

    2014-01-01

    Wearable EEG systems for continuous brain monitoring is an emergent technology that involves significant technical challenges. Some of these are related to the fact that these systems operate in conditions that are far less controllable with respect to interference and artifacts than is the case for conventional systems. Quantitative assessment of artifacts provides a mean for optimization with respect to electrode technology, electrode location, electronic instrumentation and system design. To this end, we propose an artifact assessment method and evaluate it over an empirical study of 3 subjects and 5 different types of artifacts. The study showed consistent results across subjects and artifacts.

  16. Silicon bulk micromachined hybrid dimensional artifact.

    Energy Technology Data Exchange (ETDEWEB)

    Claudet, Andre A.; Tran, Hy D.; Bauer, Todd Marks; Shilling, Katherine Meghan; Oliver, Andrew David

    2010-03-01

    A mesoscale dimensional artifact based on silicon bulk micromachining fabrication has been developed and manufactured with the intention of evaluating the artifact both on a high precision coordinate measuring machine (CMM) and video-probe based measuring systems. This hybrid artifact has features that can be located by both a touch probe and a video probe system with a k=2 uncertainty of 0.4 {micro}m, more than twice as good as a glass reference artifact. We also present evidence that this uncertainty could be lowered to as little as 50 nm (k=2). While video-probe based systems are commonly used to inspect mesoscale mechanical components, a video-probe system's certified accuracy is generally much worse than its repeatability. To solve this problem, an artifact has been developed which can be calibrated using a commercially available high-accuracy tactile system and then be used to calibrate typical production vision-based measurement systems. This allows for error mapping to a higher degree of accuracy than is possible with a glass reference artifact. Details of the designed features and manufacturing process of the hybrid dimensional artifact are given and a comparison of the designed features to the measured features of the manufactured artifact is presented and discussed. Measurement results from vision and touch probe systems are compared and evaluated to determine the capability of the manufactured artifact to serve as a calibration tool for video-probe systems. An uncertainty analysis for calibration of the artifact using a CMM is presented.

  17. Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE.

    Directory of Open Access Journals (Sweden)

    Yanchun Liang

    Full Text Available BACKGROUND: Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits. METHODOLOGY: In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility. CONCLUSIONS: We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studying genotype-phenotype relationships. The software is freely available with source code at http://ccst.jlu.edu.cn/JCSB/RFET/.

  18. Computational prediction of microRNA genes in silkworm genome

    Institute of Scientific and Technical Information of China (English)

    TONG Chuan-zhou; JIN Yong-feng; ZHANG Yao-zhou

    2006-01-01

    MicroRNAs (miRNAs) constitute a novel, extensive class of small RNAs (~21 nucleotides), and play important gene-regulation roles during growth and development in various organisms. Here we conducted a homology search to identify homologs of previously validated miRNAs from silkworm genome. We identified 24 potential miRNA genes, and gave each of them a name according to the common criteria. Interestingly, we found that a great number of newly identified miRNAs were conserved in silkworm and Drosophila, and family alignment revealed that miRNA families might possess single nucleotide polymorphisms. miRNA gene clusters and possible functions of complement miRNA pairs are discussed.

  19. A brain region-specific predictive gene map for autism derived by profiling a reference gene set.

    Directory of Open Access Journals (Sweden)

    Ajay Kumar

    Full Text Available Molecular underpinnings of complex psychiatric disorders such as autism spectrum disorders (ASD remain largely unresolved. Increasingly, structural variations in discrete chromosomal loci are implicated in ASD, expanding the search space for its disease etiology. We exploited the high genetic heterogeneity of ASD to derive a predictive map of candidate genes by an integrated bioinformatics approach. Using a reference set of 84 Rare and Syndromic candidate ASD genes (AutRef84, we built a composite reference profile based on both functional and expression analyses. First, we created a functional profile of AutRef84 by performing Gene Ontology (GO enrichment analysis which encompassed three main areas: 1 neurogenesis/projection, 2 cell adhesion, and 3 ion channel activity. Second, we constructed an expression profile of AutRef84 by conducting DAVID analysis which found enrichment in brain regions critical for sensory information processing (olfactory bulb, occipital lobe, executive function (prefrontal cortex, and hormone secretion (pituitary. Disease specificity of this dual AutRef84 profile was demonstrated by comparative analysis with control, diabetes, and non-specific gene sets. We then screened the human genome with the dual AutRef84 profile to derive a set of 460 potential ASD candidate genes. Importantly, the power of our predictive gene map was demonstrated by capturing 18 existing ASD-associated genes which were not part of the AutRef84 input dataset. The remaining 442 genes are entirely novel putative ASD risk genes. Together, we used a composite ASD reference profile to generate a predictive map of novel ASD candidate genes which should be prioritized for future research.

  20. Gentrepid V2.0: A web server for candidate disease gene prediction

    NARCIS (Netherlands)

    Ballouz, S.; Liu, J.Y.; George, R.A.; Bains, N.; Liu, A.; Oti, M.O.; Gaeta, B.; Fatkin, D.; Wouters, M.A.

    2013-01-01

    BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that c

  1. PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes.

    Science.gov (United States)

    Fang, Li; Zhang, Man; Li, Yanhui; Liu, Yan; Cui, Qinghua; Wang, Nanping

    2016-01-01

    The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, and wound healing. Here, we describe the first database of PPAR target genes, PPARgene. Among the 225 experimentally verified PPAR target genes, 83 are for PPARα, 83 are for PPARβ/δ, and 104 are for PPARγ. Detailed information including tissue types, species, and reference PubMed IDs was also provided. In addition, we developed a machine learning method to predict novel PPAR target genes by integrating in silico PPAR-responsive element (PPRE) analysis with high throughput gene expression data. Fivefold cross validation showed that the performance of this prediction method was significantly improved compared to the in silico PPRE analysis method. The prediction tool is also implemented in the PPARgene database.

  2. Neural network predicts sequence of TP53 gene based on DNA chip

    DEFF Research Database (Denmark)

    Spicker, J.S.; Wikman, F.; Lu, M.L.;

    2002-01-01

    We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero...... and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence....

  3. Algorithm for Finding Optimal Gene Sets in Microarray Prediction

    CERN Document Server

    Deutsch, J M

    2001-01-01

    Motivation: Microarray data has been recently been shown to be efficacious in distinguishing closely related cell types that often appear in the diagnosis of cancer. It is useful to determine the minimum number of genes needed to do such a diagnosis both for clinical use and to determine the importance of specific genes for cancer. Here a replication algorithm is used for this purpose. It evolves an ensemble of predictors, all using different combinations of genes to generate a set of optimal predictors. Results: We apply this method to the leukemia data of the Whitehead/MIT group that attempts to differentially diagnose two kinds of leukemia, and also to data of Khan et. al. to distinguish four different kinds of childhood cancers. In the latter case we were able to reduce the number of genes needed from 96 down to 15, while at the same time being able to perfectly classify all of their test data. Availability: http://stravinsky.ucsc.edu/josh/gesses/ Contact: josh@physics.ucsc.edu

  4. A particle accelerator probes artifacts

    CERN Document Server

    Dran, J C; Salomon, J

    2002-01-01

    The AGLAE system is made up of a 2 mega volts electrostatic accelerator and of 3 irradiation lines: one leads to a vacuum enclosure in which targets are irradiated and the 2 others lines are designed to irradiate targets under an air or helium atmosphere. The AGLAE system is located in the premises of the Louvre museum in Paris and is devoted to the study of cultural objects through ion beam analysis (IBA). 4 techniques are used: -) proton-induced X-ray emission (PIXE) -) proton-induced gamma ray (PIGE) -) Rutherford backscattering spectrometry (NRS) and -) nuclear reaction analysis (NRA). A decisive progress has permitted the direct analysis of artifacts without sampling. The object itself is set just a few millimeters away from the exit window of the beam in an air or helium atmosphere. The exit window must be resistant enough to bear the atmospheric pressure and the damages caused by the proton beam but must be thin enough to not deteriorate the quality of the beam. By using a 10 sup - sup 7 m thick exit w...

  5. Analysis and prediction of gene splice sites in four Aspergillus genomes

    DEFF Research Database (Denmark)

    Wang, Kai; Ussery, David; Brunak, Søren

    2009-01-01

    , 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...

  6. A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients.

    Directory of Open Access Journals (Sweden)

    Christian Damasco

    Full Text Available INTRODUCTION: The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures. METHODS: We thus constructed a gene expression signature (the DM signature using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes or mitotic division (72 genes. RESULTS: The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis. CONCLUSION: This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.

  7. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling

    NARCIS (Netherlands)

    R.G.W. Verhaak (Roel); B.J. Wouters (Bas); C.A.J. Erpelinck (Claudia); S. Abbas (Saman); H.B. Beverloo (Berna); S. Lugthart (Sanne); B. Löwenberg (Bob); H.R. Delwel (Ruud); P.J.M. Valk (Peter)

    2009-01-01

    textabstractWe examined the gene expression profiles of two independent cohorts of patients with acute myeloid leukemia [n=247 and n=214 (younger than or equal to 60 years)] to study the applicability of gene expression profiling as a single assay in prediction of acute myeloid leukemia-specific mol

  8. Colorectal Cancer "Methylator Phenotype": Fact or Artifact?

    Directory of Open Access Journals (Sweden)

    Charles Anacleto

    2005-04-01

    Full Text Available It has been proposed that human colorectal tumors can be classified into two groups: one in which methylation is rare, and another with methylation of several loci associated with a "CpG island methylated phenotype (CIMP," characterized by preferential proximal location in the colon, but otherwise poorly defined. There is considerable overlap between this putative methylator phenotype and the well-known mutator phenotype associated with microsatellite instability (MSI. We have examined hypermethylation of the promoter region of five genes (DAPK, MGMT, hMLH1, p16INK4a, and p14ARF in 106 primary colorectal cancers. A graph depicting the frequency of methylated loci in the series of tumors showed a continuous, monotonically decreasing distribution quite different from the previously claimed discontinuity. We observed a significant association between the presence of three or more methylated loci and the proximal location of the tumors. However, if we remove from analysis the tumors with hMLH1 methylation or those with MSI, the significance vanishes, suggesting that the association between multiple methylations and proximal location was indirect due to the correlation with MSI. Thus, our data do not support the independent existence of the so-called methylator phenotype and suggest that it rather may represent a statistical artifact caused by confounding of associations.

  9. Mammographic Artifacts on Full-Field Digital Mammography

    OpenAIRE

    Choi, Jae Jeong; Kim, Sung Hun; Kang, Bong Joo; Choi, Byung Gil; Song, ByungJoo; Jung, Haijo

    2013-01-01

    This study investigates the incidence of full-field digital mammographic (FFDM) artifacts with three systems at two institutions and compares the artifacts between two detector types and two grid types. A total of 4,440 direct and 4,142 indirect FFDM images were reviewed by two radiologists, and artifacts were classified as patient related, hardware related, and software processing. The overall incidence of FFDM artifacts was 3.4 % (292/8,582). Patient related artifacts (motion artifacts and ...

  10. Computational prediction of essential genes in an unculturable endosymbiotic bacterium, Wolbachia of Brugia malayi

    Directory of Open Access Journals (Sweden)

    Carlow Clotilde KS

    2009-11-01

    Full Text Available Abstract Background Wolbachia (wBm is an obligate endosymbiotic bacterium of Brugia malayi, a parasitic filarial nematode of humans and one of the causative agents of lymphatic filariasis. There is a pressing need for new drugs against filarial parasites, such as B. malayi. As wBm is required for B. malayi development and fertility, targeting wBm is a promising approach. However, the lifecycle of neither B. malayi nor wBm can be maintained in vitro. To facilitate selection of potential drug targets we computationally ranked the wBm genome based on confidence that a particular gene is essential for the survival of the bacterium. Results wBm protein sequences were aligned using BLAST to the Database of Essential Genes (DEG version 5.2, a collection of 5,260 experimentally identified essential genes in 15 bacterial strains. A confidence score, the Multiple Hit Score (MHS, was developed to predict each wBm gene's essentiality based on the top alignments to essential genes in each bacterial strain. This method was validated using a jackknife methodology to test the ability to recover known essential genes in a control genome. A second estimation of essentiality, the Gene Conservation Score (GCS, was calculated on the basis of phyletic conservation of genes across Wolbachia's parent order Rickettsiales. Clusters of orthologous genes were predicted within the 27 currently available complete genomes. Druggability of wBm proteins was predicted by alignment to a database of protein targets of known compounds. Conclusion Ranking wBm genes by either MHS or GCS predicts and prioritizes potentially essential genes. Comparison of the MHS to GCS produces quadrants representing four types of predictions: those with high confidence of essentiality by both methods (245 genes, those highly conserved across Rickettsiales (299 genes, those similar to distant essential genes (8 genes, and those with low confidence of essentiality (253 genes. These data facilitate

  11. Coulomb Artifacts and Bottomonium Hyperfine Splitting in Lattice NRQCD

    CERN Document Server

    Liu, Tao; Rayyan, Ahmed

    2016-01-01

    We study the role of the lattice artifacts associated with the Coulomb binding effects in the analysis of the heavy quarkonium within lattice NRQCD. We find that a "na\\"ive" perturbative matching generates spurious linear Coulomb artifacts, which result in a large systematic error in the lattice predictions for the heavy quarkonium spectrum. This effect is responsible, in particular, for the discrepancy between the recent determinations of the bottomonium hyperfine splitting in the radiatively improved lattice NRQCD [1, 2]. We show that the correct matching procedure which provides full control over discretization errors is based on the asymptotic expansion of the lattice theory about the continuum limit, which gives $M_{\\Upsilon(1S)}-M_{\\eta_b(1S)}=52.9\\pm 5.5~{\\rm MeV}$ [1].

  12. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling.

    Science.gov (United States)

    Verhaak, Roel G W; Wouters, Bas J; Erpelinck, Claudia A J; Abbas, Saman; Beverloo, H Berna; Lugthart, Sanne; Löwenberg, Bob; Delwel, Ruud; Valk, Peter J M

    2009-01-01

    We examined the gene expression profiles of two independent cohorts of patients with acute myeloid leukemia [n=247 and n=214 (younger than or equal to 60 years)] to study the applicability of gene expression profiling as a single assay in prediction of acute myeloid leukemia-specific molecular subtypes. The favorable cytogenetic acute myeloid leukemia subtypes, i.e., acute myeloid leukemia with t(8;21), t(15;17) or inv(16), were predicted with maximum accuracy (positive and negative predictive value: 100%). Mutations in NPM1 and CEBPA were predicted less accurately (positive predictive value: 66% and 100%, and negative predictive value: 99% and 97% respectively). Various other characteristic molecular acute myeloid leukemia subtypes, i.e., mutant FLT3 and RAS, abnormalities involving 11q23, -5/5q-, -7/7q-, abnormalities involving 3q (abn3q) and t(9;22), could not be correctly predicted using gene expression profiling. In conclusion, gene expression profiling allows accurate prediction of certain acute myeloid leukemia subtypes, e.g. those characterized by expression of chimeric transcription factors. However, detection of mutations affecting signaling molecules and numerical abnormalities still requires alternative molecular methods.

  13. Identifying Gene Regulatory Networks in Arabidopsis by In Silico Prediction, Yeast-1-Hybrid, and Inducible Gene Profiling Assays.

    Science.gov (United States)

    Sparks, Erin E; Benfey, Philip N

    2016-01-01

    A system-wide understanding of gene regulation will provide deep insights into plant development and physiology. In this chapter we describe a threefold approach to identify the gene regulatory networks in Arabidopsis thaliana that function in a specific tissue or biological process. Since no single method is sufficient to establish comprehensive and high-confidence gene regulatory networks, we focus on the integration of three approaches. First, we describe an in silico prediction method of transcription factor-DNA binding, then an in vivo assay of transcription factor-DNA binding by yeast-1-hybrid and lastly the identification of co-expression clusters by transcription factor induction in planta. Each of these methods provides a unique tool to advance our understanding of gene regulation, and together provide a robust model for the generation of gene regulatory networks.

  14. Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.

    Science.gov (United States)

    Grieb, Melanie; Burkovski, Andre; Sträng, J Eric; Kraus, Johann M; Groß, Alexander; Palm, Günther; Kühl, Michael; Kestler, Hans A

    2015-01-01

    Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

  15. Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Shuhei Kaneko

    2015-01-01

    Full Text Available In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso has been widely used to select genes that truly correlated with a patient’s survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV. However, this method can pass over (or fail to identify true positive genes (i.e., it identifies false negatives in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.

  16. Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings.

    Science.gov (United States)

    Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

    2016-01-01

    Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

  17. Prediction of gene-phenotype associations in humans, mice, and plants using phenologs.

    Science.gov (United States)

    Woods, John O; Singh-Blom, Ulf Martin; Laurent, Jon M; McGary, Kriston L; Marcotte, Edward M

    2013-06-21

    Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such "orthologous phenotypes," or "phenologs," are examples of deep homology, and may be used to predict additional candidate disease genes. In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data--from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans--establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene-phenotype associations, as for the Arabidopsis response to vernalization phenotype. We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.

  18. Combined effects of thrombosis pathway gene variants predict cardiovascular events.

    Directory of Open Access Journals (Sweden)

    Kirsi Auro

    2007-07-01

    Full Text Available The genetic background of complex diseases is proposed to consist of several low-penetrance risk loci. Addressing this complexity likely requires both large sample size and simultaneous analysis of different predisposing variants. We investigated the role of four thrombosis genes: coagulation factor V (F5, intercellular adhesion molecule 1 (ICAM1, protein C (PROC, and thrombomodulin (THBD in cardiovascular diseases. Single allelic gene variants and their pair-wise combinations were analyzed in two independently sampled population cohorts from Finland. From among 14,140 FINRISK participants (FINRISK-92, n = 5,999 and FINRISK-97, n = 8,141, we selected for genotyping a sample of 2,222, including 528 incident cardiovascular disease (CVD cases and random subcohorts totaling 786. To cover all known common haplotypes (>10%, 54 single nucleotide polymorphisms (SNPs were genotyped. Classification-tree analysis identified 11 SNPs that were further analyzed in Cox's proportional hazard model as single variants and pair-wise combinations. Multiple testing was controlled by use of two independent cohorts and with false-discovery rate. Several CVD risk variants were identified: In women, the combination of F5 rs7542281 x THBD rs1042580, together with three single F5 SNPs, was associated with CVD events. Among men, PROC rs1041296, when combined with either ICAM1 rs5030341 or F5 rs2269648, was associated with total mortality. As a single variant, PROC rs1401296, together with the F5 Leiden mutation, was associated with ischemic stroke events. Our strategy to combine the classification-tree analysis with more traditional genetic models was successful in identifying SNPs-acting either in combination or as single variants--predisposing to CVD, and produced consistent results in two independent cohorts. These results suggest that variants in these four thrombosis genes contribute to arterial cardiovascular events at population level.

  19. Accurate prediction of secondary metabolite gene clusters in filamentous fungi

    DEFF Research Database (Denmark)

    Andersen, Mikael Rørdam; Nielsen, Jakob Blæsbjerg; Klitgaard, Andreas

    2013-01-01

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify suppo...... used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom....

  20. An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

    Science.gov (United States)

    Koot, Yvonne E. M.; van Hooff, Sander R.; Boomsma, Carolien M.; van Leenen, Dik; Groot Koerkamp, Marian J. A.; Goddijn, Mariëtte; Eijkemans, Marinus J. C.; Fauser, Bart C. J. M.; Holstege, Frank C. P.; Macklon, Nick S.

    2016-01-01

    The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention. PMID:26797113

  1. SCGPred: A Score-based Method for Gene Structure Prediction by Combining Multiple Sources of Evidence

    Institute of Scientific and Technical Information of China (English)

    Xiao Li; Qingan Ren; Yang Weng; Haoyang Cai; Yunmin Zhu; Yizheng Zhang

    2008-01-01

    Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly sequenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred,to improve the accuracy of prediction by combining multiple sources of evidence.SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http://bio.scu.edu.cn/SCGPred/.

  2. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

    Directory of Open Access Journals (Sweden)

    Daniel S Himmelstein

    2015-07-01

    Full Text Available The first decade of Genome Wide Association Studies (GWAS has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79 from a withheld multiple sclerosis (MS GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3 validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io. Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach

  3. The Ambivalent Ontology of Digital Artifacts

    DEFF Research Database (Denmark)

    Kallinikos, Jannis; Aaltonen, Aleksi; Marton, Attila

    2013-01-01

    Digital artifacts are embedded in wider and constantly shifting ecosystems such that they become increasingly editable, interactive, reprogrammable, and distributable. This state of flux and constant transfiguration renders the value and utility of these artifacts contingent on shifting webs...... of functional relations with other artifacts across specific contexts and organizations. By the same token, it apportions control over the development and use of these artifacts over a range of dispersed stakeholders and makes their management a complex technical and social undertaking. These ideas...... are illustrated with reference to (1) provenance and authenticity of digital documents within the overall context of archiving and social memory and (2) the content dynamics occasioned by the findability of content mediated by Internet search engines. We conclude that the steady change and transfiguration...

  4. Manteia, a predictive data mining system for vertebrate genes and its applications to human genetic diseases.

    Science.gov (United States)

    Tassy, Olivier; Pourquié, Olivier

    2014-01-01

    The function of genes is often evolutionarily conserved, and comparing the annotation of ortholog genes in different model organisms has proved to be a powerful predictive tool to identify the function of human genes. Here, we describe Manteia, a resource available online at http://manteia.igbmc.fr. Manteia allows the comparison of embryological, expression, molecular and etiological data from human, mouse, chicken and zebrafish simultaneously to identify new functional and structural correlations and gene-disease associations. Manteia is particularly useful for the analysis of gene lists produced by high-throughput techniques such as microarrays or proteomics. Data can be easily analyzed statistically to characterize the function of groups of genes and to correlate the different aspects of their annotation. Sophisticated querying tools provide unlimited ways to merge the information contained in Manteia along with the possibility of introducing custom user-designed biological questions into the system. This allows for example to connect all the animal experimental results and annotations to the human genome, and take advantage of data not available for human to look for candidate genes responsible for genetic disorders. Here, we demonstrate the predictive and analytical power of the system to predict candidate genes responsible for human genetic diseases.

  5. Preliminary organizational culture scale focused on artifacts.

    Science.gov (United States)

    Bonavia, Tomas

    2006-12-01

    In this preliminary study, an Organizational Culture Scale was developed to assess cultural artifacts according to Schein's typology (1985). It includes a set of cultural artifacts to measure the extent to which an organization is more or less traditional. A total of 249 managers from a range of different companies responded to the items. Preliminary analysis yielded a one-dimensional scale with 14 items with high internal consistency and homogeneity.

  6. Preliminary organizational culture scale focused on artifacts

    OpenAIRE

    Bonavia, Tomas

    2006-01-01

    In this preliminary study, an organizational culture scale was developed to assess cultural artifacts according to Schein´s typology (1985). It includes a set of cultural artifacts to measure the extent to which an organization is more or less traditional. A total of 249 managers from a range of different companies responded to the items. Preliminary analysis yielded a one-dimensional scale with 14 items with high internal consistency and homogeneity.

  7. Predicting Polymerase Ⅱ Core Promoters by Cooperating Transcription Factor Binding Sites in Eukaryotic Genes

    Institute of Scientific and Technical Information of China (English)

    Xiao-Tu MA; Min-Ping QIAN; Hai-Xu TANG

    2004-01-01

    Several discriminate functions for predicting core promoters that based on the potential cooperation between transcription factor binding sites (TFBSs) are discussed. It is demonstrated that the promoter predicting accuracy is improved when the cooperation among TFBSs is taken into consideration.The core promoter region of a newly discovered gene CKLFSF1 is predicted to locate more than 1.5 kb far away from the 5′ end of the transcript and in the last intron of its upstream gene, which is experimentally confirmed later. The core promoters of 3402 human RefSeq sequences, obtained by extending the mRNAs in human genome sequences, are predicted by our algorithm, and there are about 60% of the predicted core promoters locating within the ± 500 bp region relative to the annotated transcription start site.

  8. The use of multiple hierarchically independent gene ontology terms in gene function prediction and genome annotation

    NARCIS (Netherlands)

    Kourmpetis, Y.I.A.; Burgt, van der A.; Bink, M.C.A.M.; Braak, ter C.J.F.; Ham, van R.C.H.J.

    2007-01-01

    The Gene Ontology (GO) is a widely used controlled vocabulary for the description of gene function. In this study we quantify the usage of multiple and hierarchically independent GO terms in the curated genome annotations of seven well-studied species. In most genomes, significant proportions (6 -

  9. Prediction and validation of gene-disease associations using methods inspired by social network analyses.

    Directory of Open Access Journals (Sweden)

    U Martin Singh-Blom

    Full Text Available Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques, is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall [corrected].

  10. Software Suite for Gene and Protein Annotation Prediction and Similarity Search.

    Science.gov (United States)

    Chicco, Davide; Masseroli, Marco

    2015-01-01

    In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim, a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib. polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.

  11. 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.

  12. MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

    Directory of Open Access Journals (Sweden)

    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.

  13. Challenges of incorporating gene expression data to predict HCC prognosis in the age of systems biology

    Institute of Scientific and Technical Information of China (English)

    Yan Du; Guang-Wen Cao

    2012-01-01

    Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide.The recurrence of HCC after curative treatments is currently a major hurdle.Identification of subsets of patients with distinct prognosis provides an opportunity to tailor therapeutic approaches as well as to select the patients with specific sub-phenotypes for targeted therapy.Thus,the development of gene expression profiles to improve the prediction of HCC prognosis is important for HCC management.Although several gene signatures have been evaluated for the prediction of HCC prognosis,there is no consensus on the predictive power of these signatures.Using systematic approaches to evaluate these signatures and combine them with clinicopathologic information may provide more accurate prediction of HCC prognosis.Recently,Villanueva et al[13] developed a composite prognostic model incorporating gene expression patterns in both tumor and adjacent tissues to predict HCC recurrence.In this commentary,we summarize the current progress in using gene signatures to predict HCC prognosis,and discuss the importance,existing issues and future research directions in this field.

  14. Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Maria D Chikina

    2009-06-01

    Full Text Available Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.

  15. Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

    Directory of Open Access Journals (Sweden)

    Karlsson Per

    2008-09-01

    Full Text Available Abstract Background 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. Methods 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. Results 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. Conclusion 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. Trial

  16. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

    Energy Technology Data Exchange (ETDEWEB)

    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

  17. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction*

    Science.gov (United States)

    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

    2017-01-01

    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. PMID

  18. Prediction of metastasis from low-malignant breast cancer by gene expression profiling

    DEFF Research Database (Denmark)

    Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja;

    2007-01-01

    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...... 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...... demonstrated high cross-platform consistency of the classifiers. Higher performance of HUMAC32 was demonstrated among the low-malignant cancers compared with the 70-gene classifier. This suggests that although the metastatic potential to some extend is determined by the same genes in groups of tumors...

  19. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    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

  20. Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

    OpenAIRE

    Chen, Yang; Xu, Rong

    2015-01-01

    Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cros...

  1. Formal modeling of Gene Ontology annotation predictions based on factor graphs

    Science.gov (United States)

    Spetale, Flavio; Murillo, Javier; Tapia, Elizabeth; Arce, Débora; Ponce, Sergio; Bulacio, Pilar

    2016-04-01

    Gene Ontology (GO) is a hierarchical vocabulary for gene product annotation. Its synergy with machine learning classification methods has been widely used for the prediction of protein functions. Current classification methods rely on heuristic solutions to check the consistency with some aspects of the underlying GO structure. In this work we formalize the GO is-a relationship through predicate logic. Moreover, an ontology model based on Forney Factor Graph (FFG) is shown on a general fragment of Cellular Component GO.

  2. Prediction of key genes in ovarian cancer treated with decitabine based on network strategy.

    Science.gov (United States)

    Wang, Yu-Zhen; Qiu, Sheng-Chun

    2016-06-01

    The objective of the present study was to predict key genes in ovarian cancer before and after treatment with decitabine utilizing a network approach and to reveal the molecular mechanism. Pathogenic networks of ovarian cancer before and after treatment were identified based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) detected by Significance Analysis of Microarrays (SAM) method. A weight was assigned to each gene in the pathogenic network and then candidate genes were evaluated. Topological properties (degree, betweenness, closeness and stress) of candidate genes were analyzed to investigate more confident pathogenic genes. Pathway enrichment analysis for candidate and seed genes were conducted. Validation of candidate gene expression in ovarian cancer was performed by reverse transcriptase-polymerase chain reaction (RT-PCR) assays. There were 73 nodes and 147 interactions in the pathogenic network before treatment, while 47 nodes and 66 interactions after treatment. A total of 32 candidate genes were identified in the before treatment group of ovarian cancer, of which 16 were rightly candidate genes after treatment and the others were silenced. We obtained 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) for decitabine treatment that were validated by RT-PCR. In conclusion, we successfully identified 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) and validated them, which provides insight into the molecular mechanisms of decitabine treatment and may be potential pathogenic biomarkers for the therapy of ovarian cancer.

  3. Reconstructing design, explaining artifacts: philosophical reflections on the design and explanation of technical artifacts

    NARCIS (Netherlands)

    De Ridder, G.J.

    2007-01-01

    Philosophers of science have by and large neglected technology. In this book, I have tried to do something about this lacuna by analyzing a few aspects of technical artifacts from a philosophical angle. The project was part of the research program "The Dual Nature of Technical Artifacts" based at De

  4. Use of Information Measures and Their Approximations to Detect Predictive Gene-Gene Interaction

    Directory of Open Access Journals (Sweden)

    Jan Mielniczuk

    2017-01-01

    Full Text Available We reconsider the properties and relationships of the interaction information and its modified versions in the context of detecting the interaction of two SNPs for the prediction of a binary outcome when interaction information is positive. This property is called predictive interaction, and we state some new sufficient conditions for it to hold true. We also study chi square approximations to these measures. It is argued that interaction information is a different and sometimes more natural measure of interaction than the logistic interaction parameter especially when SNPs are dependent. We introduce a novel measure of predictive interaction based on interaction information and its modified version. In numerical experiments, which use copulas to model dependence, we study examples when the logistic interaction parameter is zero or close to zero for which predictive interaction is detected by the new measure, while it remains undetected by the likelihood ratio test.

  5. A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Jason Ernst

    2008-03-01

    Full Text Available While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer, a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.

  6. MPEG recompression detection based on block artifacts

    Science.gov (United States)

    Luo, Weiqi; Wu, Min; Huang, Jiwu

    2008-02-01

    With sophisticated video editing technologies, it is becoming increasingly easy to tamper digital video without leaving visual clues. One of the common tampering operations on video is to remove some frames and then re-encode the resulting video. In this paper, we propose a new method for detecting this type of tampering by exploring the temporal patterns of the block artifacts in video sequences. We show that MPEG compression introduces different block artifacts into various types of frames and that the strength of the block artifacts as a function over time has a regular pattern for a given group of pictures (GOP) structure. When some frames are removed from an MPEG video file and the file is then recompressed, the block artifacts introduced by the previous compression would remain and affect the average of block artifact strength of the recompressed one in such a way that depends on the number of deleted frames and the type of GOP used previously. We propose a feature curve to reveal the compression history of an MPEG video file with a given GOP structure, and use it as evidence to detect tampering. Experimental results evaluated on common video benchmark clips demonstrate the effectiveness of the proposed method.

  7. Mediating Artifact in Teacher Professional Development

    Science.gov (United States)

    Svendsen, Bodil

    2015-07-01

    This article focuses on teacher professional development (TPD) in natural science through the 5E model as mediating artifact. The study was conducted in an upper secondary school, grounded in a school-based intervention research project. My contribution to the field of research on TPD is founded on the hypothesis that teachers would be best facilitated to make their practice more inquiry based if they are provided with a mediating artifact. In this study the artifact is a model 5E, which is a conceptual way of thinking, to help teachers reflect on their practice. The aim is to encourage teachers to make changes themselves, by applying extended use of inquiry into their practice. This mediated artifact could thus be used across different national contexts. The main research question is; how can the 5E model as a mediating artifact enhance TPD? The article addresses the processes of the use of the 5E model and its influence on teachers' perception of the model. This is in order for teachers to conceptualize their goals related to inquiry and scientific thinking, and to solve the problems involved in achieving those goals in their own contexts. The study concludes that, after the intervention, the teachers' approaches and strategies demonstrate greater emphasis on learning.

  8. A classification-based framework for predicting and analyzing gene regulatory response.

    Science.gov (United States)

    Kundaje, Anshul; Middendorf, Manuel; Shah, Mihir; Wiggins, Chris H; Freund, Yoav; Leslie, Christina

    2006-03-20

    We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem--predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree. In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more

  9. Distance in cancer gene expression from stem cells predicts patient survival.

    Science.gov (United States)

    Riester, Markus; Wu, Hua-Jun; Zehir, Ahmet; Gönen, Mithat; Moreira, Andre L; Downey, Robert J; Michor, Franziska

    2017-01-01

    The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of

  10. Prediction and experimental validation of novel STAT3 target genes in human cancer cells.

    Directory of Open Access Journals (Sweden)

    Young Min Oh

    Full Text Available The comprehensive identification of functional transcription factor binding sites (TFBSs is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.

  11. Can Thrifty Gene(s) or Predictive Fetal Programming for Thriftiness Lead to Obesity?

    Science.gov (United States)

    Baig, Ulfat; Belsare, Prajakta; Watve, Milind; Jog, Maithili

    2011-01-01

    Obesity and related disorders are thought to have their roots in metabolic "thriftiness" that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of a mathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that a thrifty gene cannot exist in a stable polymorphic state in a population. The conditions for evolution of thrifty fetal programming are restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in natural courses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affecting nutrition that can result in a positive correlation.

  12. Can Thrifty Gene(s or Predictive Fetal Programming for Thriftiness Lead to Obesity?

    Directory of Open Access Journals (Sweden)

    Ulfat Baig

    2011-01-01

    Full Text Available Obesity and related disorders are thought to have their roots in metabolic “thriftiness” that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of a mathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that a thrifty gene cannot exist in a stable polymorphic state in a population. The conditions for evolution of thrifty fetal programming are restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in natural courses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affecting nutrition that can result in a positive correlation.

  13. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Directory of Open Access Journals (Sweden)

    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.

  14. An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction.

    Science.gov (United States)

    Piro, Rosario Michael; Ala, Ugo; Molineris, Ivan; Grassi, Elena; Bracco, Chiara; Perego, Gian Paolo; Provero, Paolo; Di Cunto, Ferdinando

    2011-11-01

    Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.

  15. A Language of Objects and Artifacts

    DEFF Research Database (Denmark)

    Svabo, Connie

    This is a conceptual inquiry about materiality. It gives an introductory overview to the vocabulary of materiality in a chosen selection of theories. The paper shows a language of artifacts and objects as it is used within practice-based approaches to organizational knowing. The examined intellec......This is a conceptual inquiry about materiality. It gives an introductory overview to the vocabulary of materiality in a chosen selection of theories. The paper shows a language of artifacts and objects as it is used within practice-based approaches to organizational knowing. The examined...... intellectual traditions are interpretive-cultural approaches; activity theory; and sociology of translation. Similarities and differences are presented in the way these three distinct intellectual traditions conceptualize the array of material objects and artifacts which are central in the tales of practice...

  16. Epigenomic modifications predict active promoters and gene structure in Toxoplasma gondii.

    Directory of Open Access Journals (Sweden)

    Mathieu Gissot

    2007-06-01

    Full Text Available Mechanisms of gene regulation are poorly understood in Apicomplexa, a phylum that encompasses deadly human pathogens like Plasmodium and Toxoplasma. Initial studies suggest that epigenetic phenomena, including histone modifications and chromatin remodeling, have a profound effect upon gene expression and expression of virulence traits. Using the model organism Toxoplasma gondii, we characterized the epigenetic organization and transcription patterns of a contiguous 1% of the T. gondii genome using custom oligonucleotide microarrays. We show that methylation and acetylation of histones H3 and H4 are landmarks of active promoters in T. gondii that allow us to deduce the position and directionality of gene promoters with >95% accuracy. These histone methylation and acetylation "activation" marks are strongly associated with gene expression. We also demonstrate that the pattern of histone H3 arginine methylation distinguishes certain promoters, illustrating the complexity of the histone modification machinery in Toxoplasma. By integrating epigenetic data, gene prediction analysis, and gene expression data from the tachyzoite stage, we illustrate feasibility of creating an epigenomic map of T. gondii tachyzoite gene expression. Further, we illustrate the utility of the epigenomic map to empirically and biologically annotate the genome and show that this approach enables identification of previously unknown genes. Thus, our epigenomics approach provides novel insights into regulation of gene expression in the Apicomplexa. In addition, with its compact genome, genetic tractability, and discrete life cycle stages, T. gondii provides an important new model to study the evolutionarily conserved components of the histone code.

  17. Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen.

    Science.gov (United States)

    Kok, Marleen; Linn, Sabine C; Van Laar, Ryan K; Jansen, Maurice P H M; van den Berg, Teun M; Delahaye, Leonie J M J; Glas, Annuska M; Peterse, Johannes L; Hauptmann, Michael; Foekens, John A; Klijn, Jan G M; Wessels, Lodewyk F A; Van't Veer, Laura J; Berns, Els M J J

    2009-01-01

    Molecular signatures that predict outcome in tamoxifen treated breast cancer patients have been identified. For the first time, we compared these response profiles in an independent cohort of (neo)adjuvant systemic treatment naïve breast cancer patients treated with first-line tamoxifen for metastatic disease. From a consecutive series of 246 estrogen receptor (ER) positive primary tumors, gene expression profiling was performed on available frozen tumors using 44K oligoarrays (n = 69). A 78-gene tamoxifen response profile (formerly consisting of 81 cDNA-clones), a 21-gene set (microarray-based Recurrence Score), as well as the HOXB13-IL17BR ratio (Two-Gene-Index, RT-PCR) were analyzed. Performance of signatures in relation to time to progression (TTP) was compared with standard immunohistochemical (IHC) markers: ER, progesterone receptor (PgR) and HER2. In univariate analyses, the 78-gene tamoxifen response profile, 21-gene set and HOXB13-IL17BR ratio were all significantly associated with TTP with hazard ratios of 2.2 (95% CI 1.3-3.7, P = 0.005), 2.3 (95% CI 1.3-4.0, P = 0.003) and 4.2 (95% CI 1.4-12.3, P = 0.009), respectively. The concordance among the three classifiers was relatively low, they classified only 45-61% of patients in the same category. In multivariate analyses, the association remained significant for the 78-gene profile and the 21-gene set after adjusting for ER and PgR. The 78-gene tamoxifen response profile, the 21-gene set and the HOXB13-IL17BR ratio were all significantly associated with TTP in an independent patient series treated with tamoxifen. The addition of multigene assays to ER (IHC) improves the prediction of outcome in tamoxifen treated patients and deserves incorporation in future clinical studies.

  18. Learning "graph-mer" motifs that predict gene expression trajectories in development.

    Directory of Open Access Journals (Sweden)

    Xuejing Li

    2010-04-01

    Full Text Available A key problem in understanding transcriptional regulatory networks is deciphering what cis regulatory logic is encoded in gene promoter sequences and how this sequence information maps to expression. A typical computational approach to this problem involves clustering genes by their expression profiles and then searching for overrepresented motifs in the promoter sequences of genes in a cluster. However, genes with similar expression profiles may be controlled by distinct regulatory programs. Moreover, if many gene expression profiles in a data set are highly correlated, as in the case of whole organism developmental time series, it may be difficult to resolve fine-grained clusters in the first place. We present a predictive framework for modeling the natural flow of information, from promoter sequence to expression, to learn cis regulatory motifs and characterize gene expression patterns in developmental time courses. We introduce a cluster-free algorithm based on a graph-regularized version of partial least squares (PLS regression to learn sequence patterns--represented by graphs of k-mers, or "graph-mers"--that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, we found that the first and second latent PLS factors mapped to expression profiles for oocyte and sperm genes, respectively. We extracted both known and novel motifs from the graph-mers associated to these germline-specific patterns, including novel CG-rich motifs specific to oocyte genes. We found evidence supporting the functional relevance of these putative regulatory elements through analysis of positional bias, motif conservation and in situ gene expression. This study demonstrates that our regression model can learn biologically meaningful latent structure and identify potentially functional motifs from subtle developmental time course expression data.

  19. MRI-Based Computed Tomography Metal Artifact Correction Method for Improving Proton Range Calculation Accuracy

    Energy Technology Data Exchange (ETDEWEB)

    Park, Peter C. [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Schreibmann, Eduard; Roper, Justin; Elder, Eric; Crocker, Ian [Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia (United States); Fox, Tim [Varian Medical Systems, Palo Alto, California (United States); Zhu, X. Ronald [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Dong, Lei [Scripps Proton Therapy Center, San Diego, California (United States); Dhabaan, Anees, E-mail: anees.dhabaan@emory.edu [Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia (United States)

    2015-03-15

    Purpose: Computed tomography (CT) artifacts can severely degrade dose calculation accuracy in proton therapy. Prompted by the recently increased popularity of magnetic resonance imaging (MRI) in the radiation therapy clinic, we developed an MRI-based CT artifact correction method for improving the accuracy of proton range calculations. Methods and Materials: The proposed method replaces corrupted CT data by mapping CT Hounsfield units (HU number) from a nearby artifact-free slice, using a coregistered MRI. MRI and CT volumetric images were registered with use of 3-dimensional (3D) deformable image registration (DIR). The registration was fine-tuned on a slice-by-slice basis by using 2D DIR. Based on the intensity of paired MRI pixel values and HU from an artifact-free slice, we performed a comprehensive analysis to predict the correct HU for the corrupted region. For a proof-of-concept validation, metal artifacts were simulated on a reference data set. Proton range was calculated using reference, artifactual, and corrected images to quantify the reduction in proton range error. The correction method was applied to 4 unique clinical cases. Results: The correction method resulted in substantial artifact reduction, both quantitatively and qualitatively. On respective simulated brain and head and neck CT images, the mean error was reduced from 495 and 370 HU to 108 and 92 HU after correction. Correspondingly, the absolute mean proton range errors of 2.4 cm and 1.7 cm were reduced to less than 2 mm in both cases. Conclusions: Our MRI-based CT artifact correction method can improve CT image quality and proton range calculation accuracy for patients with severe CT artifacts.

  20. Studying the Creation of Design Artifacts

    DEFF Research Database (Denmark)

    Davis, Christopher J.; Hevner, Ala n R.; Weber, Barbara

    2016-01-01

    and information systems represents a highly interconnected locus in which both the generative processes of building design artifacts and articulating constructs used to evaluate their quality take place. We address this interconnectedness with an extended process-oriented research design enabling multi......-modal neurophysiological data analyses. We posit that our research will provide more comprehensive assessments of the efficacy of design processes and the evaluation of the qualities of the resulting design artifacts.......As software and information systems (IS) increase in functional sophistication, perceptions of IS quality are changing. Moving beyond issues of performance efficiency, essential qualities such as fitness for purpose, sustainability, and overall effectiveness become more complex. Creating software...

  1. Artifacts of Functional Electrical Stimulation on Electromyograph

    Institute of Scientific and Technical Information of China (English)

    DUAN Ren-quan; ZHANG Ding-guo

    2014-01-01

    The purpose of this study is to investigate different factors of the artifact in surface electromyography (EMG) signal caused by functional electrical stimulation (FES). The factors investigated include the size of stimulation electrode pads, the amplitude, frequency, and pulse width of the stimulation waveform and the detecting electrode points. We calculate the root mean square (RMS) of EMG signal to analyze the effect of these factors on the M-wave properties. The results indicate that the M-wave mainly depends on the stimulation amplitude and the distribution of detecting electrodes, but not on the other factors. This study can assist the reduction of artifact and the selection of detecting electrode points.

  2. Prediction of Sinorhizobium meliloti sRNA genes and experimental detection in strain 2011

    Directory of Open Access Journals (Sweden)

    Becker Anke

    2008-09-01

    Full Text Available Abstract Background Small non-coding RNAs (sRNAs have emerged as ubiquitous regulatory elements in bacteria and other life domains. However, few sRNAs have been identified outside several well-studied species of gamma-proteobacteria and thus relatively little is known about the role of RNA-mediated regulation in most other bacterial genera. Here we have conducted a computational prediction of putative sRNA genes in intergenic regions (IgRs of the symbiotic α-proteobacterium S. meliloti 1021 and experimentally confirmed the expression of dozens of these candidate loci in the closely related strain S. meliloti 2011. Results Our first sRNA candidate compilation was based mainly on the output of the sRNAPredictHT algorithm. A thorough manual sequence analysis of the curated list rendered an initial set of 18 IgRs of interest, from which 14 candidates were detected in strain 2011 by Northern blot and/or microarray analysis. Interestingly, the intracellular transcript levels varied in response to various stress conditions. We developed an alternative computational method to more sensitively predict sRNA-encoding genes and score these predicted genes based on several features to allow identification of the strongest candidates. With this novel strategy, we predicted 60 chromosomal independent transcriptional units that, according to our annotation, represent strong candidates for sRNA-encoding genes, including most of the sRNAs experimentally verified in this work and in two other contemporary studies. Additionally, we predicted numerous candidate sRNA genes encoded in megaplasmids pSymA and pSymB. A significant proportion of the chromosomal- and megaplasmid-borne putative sRNA genes were validated by microarray analysis in strain 2011. Conclusion Our data extend the number of experimentally detected S. meliloti sRNAs and significantly expand the list of putative sRNA-encoding IgRs in this and closely related α-proteobacteria. In addition, we have

  3. 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.

  4. ETS Gene Fusions as Predictive Biomarkers of Resistance to Radiation Therapy for Prostate Cancer

    Science.gov (United States)

    2016-05-01

    Award  Number:    W81XWH-10-1-0582 TITLE:       ETS Gene Fusions as Predictive Biomarkers of Resistance to Radiation Therapy for Prostate Cancer...5a.  CONTRACT  NUMBER   ETS Gene Fusions as Predictive Biomarkers of Resistance to Radiation Therapy for Prostate Cancer 5b.  GRANT  NUMBER   W81XWH...SUPPLEMENTARY  NOTES 14. ABSTRACT The  research  goals  of  this  grant  proposal  are  to:  1)  investigate  the  effect  of   ETS  gene  fusions  on  radiation

  5. The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity.

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    Slavé Petrovski

    2015-09-01

    Full Text Available Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene's proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS, termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene's regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1 genes that are known to cause disease through haploinsufficiency, 2 genes curated as dosage sensitive in ClinGen's Genome Dosage Map, 3 genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4 genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding

  6. The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity.

    Science.gov (United States)

    Petrovski, Slavé; Gussow, Ayal B; Wang, Quanli; Halvorsen, Matt; Han, Yujun; Weir, William H; Allen, Andrew S; Goldstein, David B

    2015-09-01

    Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene's proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene's regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen's Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, nc

  7. Mirror Image Video Artifact: An Under-Reported Digital Video-EEG Artifact.

    Science.gov (United States)

    Babcock, Michael A; Levis, William H; Bhatt, Amar B

    2017-01-01

    Synchronous video recording can be helpful in EEG recordings, especially in recognition of seizures and in rejection of artifacts. However, video recordings themselves are also subject to the risk of contamination by artifacts. We report a unique case in which a digital video artifact was identified, occurring during synchronous video-EEG recording, albeit independently of the EEG tracing itself. A synchronous digital video-EEG recording was performed on a 67-year-old male who presented in focal motor status epilepticus. During the initial review of the data, right-sided abnormalities on EEG apparently corresponded with (ipsilateral) right arm motor activity on video, suggesting a nonsensical anatomical localization. However, review of the patient's chart and discussion with the EEG technologist led to the recognition that the video data recorded a mirror image of the true findings of left arm motor activity. Review of the software settings led to the discovery that the video recording was inverted along the vertical axis, leading to mirror image video artifact. Recognition of this video artifact allowed for accurate interpretation of the study-that right hemispheric EEG abnormalities correlated appropriately with (contralateral) left arm twitching. Effective communication between the EEG reading physician, the treating team, and the EEG technologist is critical for recognition of such artifacts, for proper EEG interpretation, and for appropriate patient management. Mirror image video artifact affirms that bedside evaluation, astute technologists, and attentive EEG reading physicians remain important, even in the presence of video recording.

  8. 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.

  9. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

    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.

  10. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    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.

  11. Gene expression-based classification of non-small cell lung carcinomas and survival prediction.

    Directory of Open Access Journals (Sweden)

    Jun Hou

    Full Text Available BACKGROUND: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. METHODOLOGY AND PRINCIPAL FINDINGS: A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6. The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96. Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. CONCLUSIONS: The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.

  12. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data

    Science.gov (United States)

    2013-01-01

    Background 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. Results 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. Conclusions 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. PMID:24053776

  13. The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science.

    Science.gov (United States)

    Klie, Sebastian; Nikoloski, Zoran

    2012-01-01

    Since the introduction of the Gene Ontology (GO), the analysis of high-throughput data has become tightly coupled with the use of ontologies to establish associations between knowledge and data in an automated fashion. Ontologies provide a systematic description of knowledge by a controlled vocabulary of defined structure in which ontological concepts are connected by pre-defined relationships. In plant science, MapMan and GO offer two alternatives for ontology-driven analyses. Unlike GO, initially developed to characterize microbial systems, MapMan was specifically designed to cover plant-specific pathways and processes. While the dependencies between concepts in MapMan are modeled as a tree, in GO these are captured in a directed acyclic graph. Therefore, the difference in ontologies may cause discrepancies in data reduction, visualization, and hypothesis generation. Here provide the first systematic comparative analysis of GO and MapMan for the case of the model plant species Arabidopsis thaliana (Arabidopsis) with respect to their structural properties and difference in distributions of information content. In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of co-expression networks and the guilt-by-association principle. Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes. The results highlight the need for structured representation of species-specific biological knowledge, and warrants caution in the design principles employed in future ontologies.

  14. The choice between MapMan and Gene Ontology for automated gene function prediction in plant science

    Directory of Open Access Journals (Sweden)

    Sebastian eKlie

    2012-06-01

    Full Text Available Since the introduction of the Gene Ontology (GO, the analysis of high-throughput data has become tightly coupled with the use of ontologies to establish associations between knowledge and data in an automated fashion. Ontologies provide a systematic description of knowledge by a controlled vocabulary of defined structure in which ontological concepts are connected by pre-defined relationships. In plant science, MapMan and GO offer two alternatives for ontology-driven analyses. Unlike GO, initially developed to characterize microbial systems, MapMan was specifically designed to cover plant-specific pathways and processes. While the dependencies between concepts in MapMan are modeled as a tree, in GO these are captured in a directed acyclic graph. Therefore, the difference in ontologies may cause discrepancies in data reduction, visualization, and hypothesis generation. Here provide the first systematic comparative analysis of GO and MapMan for the case of the model plant species Arabidopsis thaliana (Arabidopsis with respect to their structural properties and difference in distributions of information content. In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of coexpression networks and the guilt-by-association principle. Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes. The results highlight the need for structured representation of species-specific biological knowledge, and warrants caution in the design principles employed in future ontologies.

  15. Gene panel model predictive of outcome in patients with prostate cancer.

    Science.gov (United States)

    Rabiau, Nadège; Dantal, Yann; Guy, Laurent; Ngollo, Marjolaine; Dagdemir, Aslihan; Kemeny, Jean-Louis; Terris, Benoît; Vieillefond, Annick; Boiteux, Jean-Paul; Bignon, Yves-Jean; Bernard-Gallon, Dominique

    2013-08-01

    In men at high risk for prostate cancer, established clinical and pathological parameters provide only limited prognostic information. Here we analyzed a French cohort of 103 prostate cancer patients and developed a gene panel model predictive of outcome in this group of patients. The model comprised of a 15-gene TaqMan Low-Density Array (TLDA) card, with gene expressions compared to a standardized reference. The RQ value for each gene was calculated, and a scoring system was developed. Summing all the binary scores (0 or 1) corresponding to the 15 genes, a global score is obtained between 0 and 15. This global score can be compared to Gleason score (0 to 10) by recalculating it into a 0-10 scaled score. A scaled score ≥2 suggested that the patient is suffering from a prostate cancer, and a scaled score ≥7 flagged aggressive cancer. Statistical analyses demonstrated a strongly significant linear correlation (p=3.50E-08) between scaled score and Gleason score for this prostate cancer cohort (N=103). These results support the capacity of this designed 15 target gene TLDA card approach to predict outcome in prostate cancer, opening up a new avenue for personalized medicine through future independent replication and applications for rapid identification of aggressive prostate cancer phenotypes for early intervention.

  16. HOX Gene Promoter Prediction and Inter-genomic Comparison: An Evo-Devo Study

    Directory of Open Access Journals (Sweden)

    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.

  17. Genome-wide Transcription Factor Gene Prediction and their Expressional Tissue-Specificities in Maize

    Institute of Scientific and Technical Information of China (English)

    Yi Jiang; Biao Zeng; Hainan Zhao; Mei Zhang; Shaojun Xie; Jinsheng Lai

    2012-01-01

    Transcription factors (TFs) are important regulators of gene expression.To better understand TFencoding genes in maize (Zea mays L.),a genome-wide TF prediction was performed using the updated B73 reference genome.A total of 2 298 TF genes were identified,which can be classified into 56 families.The largest family,known as the MYB superfamily,comprises 322 MYB and MYB-related TF genes.The expression patterns of 2014 (87.64%) TF genes were examined using RNA-seq data,which resulted in the identification of a subset of TFs that are specifically expressed in particular tissues (including root,shoot,leaf,ear,tassel and kernel).Similarly,98 kernel-specific TF genes were further analyzed,and it was observed that 29 of the kernel-specific genes were preferentially expressed in the early kernel developmental stage,while 69 of the genes were expressed in the late kernel developmental stage.Identification of these TFs,particularly the tissue-specific ones,provides important information for the understanding of development and transcriptional regulation of maize.

  18. The Interacting Effect of the BDNF Val66Met Polymorphism and Stressful Life Events on Adolescent Depression Is Not an Artifact of Gene-Environment Correlation: Evidence from a Longitudinal Twin Study

    Science.gov (United States)

    Chen, Jie; Li, Xinying; McGue, Matt

    2013-01-01

    Background: Confounding introduced by gene-environment correlation (rGE) may prevent one from observing a true gene-environment interaction (G × E) effect on psychopathology. The present study investigated the interacting effect of the BDNF Val66Met polymorphism and stressful life events (SLEs) on adolescent depression while controlling for the…

  19. The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer.

    Science.gov (United States)

    Hawken, Steven J; Greenwood, Celia M T; Hudson, Thomas J; Kustra, Rafal; McLaughlin, John; Yang, Quanhe; Zanke, Brent W; Little, Julian

    2010-07-01

    Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci

  20. Conceptual Model of Artifacts for Design Science Research

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2015-01-01

    We present a conceptual model of design science research artifacts. The model views an artifact at three levels. At the artifact level a selected artifact is viewed as a combination of material and immaterial aspects and a set of representations hereof. At the design level the selected artifact...... is viewed through its design in terms of descriptions, models, prototypes etc. At the knowledge level the selected artifact is viewed through ontologies, categories and various types of relevant knowledge. The model is based on description...

  1. Conceptual Model of Artifacts for Design Science Research

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2015-01-01

    We present a conceptual model of design science research artifacts. The model views an artifact at three levels. At the artifact level a selected artifact is viewed as a combination of material and immaterial aspects and a set of representations hereof. At the design level the selected artifact...... is viewed through its design in terms of descriptions, models, prototypes etc. At the knowledge level the selected artifact is viewed through ontologies, categories and various types of relevant knowledge. The model is based on description...

  2. Convergence of mutation and epigenetic alterations identifies common genes in cancer that predict for poor prognosis.

    Directory of Open Access Journals (Sweden)

    Timothy A Chan

    2008-05-01

    -wide approach, our analysis has enabled the discovery of a number of clinically significant genes targeted by multiple modes of inactivation in breast and colon cancer. Importantly, we demonstrate that a subset of these genes predict strongly for poor clinical outcome. Our data define a set of genes that are targeted by both genetic and epigenetic events, predict for clinical prognosis, and are likely fundamentally important for cancer initiation or progression.

  3. MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

    Directory of Open Access Journals (Sweden)

    Kohlbacher Oliver

    2009-09-01

    Full Text Available Abstract Background Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

  4. Predicting the size of the progeny mapping population required to positionally clone a gene.

    Science.gov (United States)

    Dinka, Stephen J; Campbell, Matthew A; Demers, Tyler; Raizada, Manish N

    2007-08-01

    A key frustration during positional gene cloning (map-based cloning) is that the size of the progeny mapping population is difficult to predict, because the meiotic recombination frequency varies along chromosomes. We describe a detailed methodology to improve this prediction using rice (Oryza sativa L.) as a model system. We derived and/or validated, then fine-tuned, equations that estimate the mapping population size by comparing these theoretical estimates to 41 successful positional cloning attempts. We then used each validated equation to test whether neighborhood meiotic recombination frequencies extracted from a reference RFLP map can help researchers predict the mapping population size. We developed a meiotic recombination frequency map (MRFM) for approximately 1400 marker intervals in rice and anchored each published allele onto an interval on this map. We show that neighborhood recombination frequencies (R-map, >280-kb segments) extracted from the MRFM, in conjunction with the validated formulas, better predicted the mapping population size than the genome-wide average recombination frequency (R-avg), with improved results whether the recombination frequency was calculated as genes/cM or kb/cM. Our results offer a detailed road map for better predicting mapping population size in diverse eukaryotes, but useful predictions will require robust recombination frequency maps based on sampling more progeny.

  5. A Social Language of Objects and Artifacts

    DEFF Research Database (Denmark)

    Svabo, Connie

    2007-01-01

    This paper is an inquiry about design. It gives an introductory overview of the vocabulary of 'materiality', which is used by a chosen selection of social theories. The paper shows a language of artifacts and objjects as it is used within practice-based approaches to knowing in organization  ...

  6. On the reduction of hypercubic lattice artifacts

    CERN Document Server

    De Soto, F

    2007-01-01

    This note presents a comparative study of various options to reduce the errors coming from the discretization of a Quantum Field Theory in a lattice with hypercubic symmetry. We show that it is possible to perform an extrapolation towards the continuum which is able to eliminate systematically the artifacts which break the O(4) symmetry.

  7. Artifacts reduction in VIR/Dawn data

    Science.gov (United States)

    Carrozzo, F. G.; Raponi, A.; De Sanctis, M. C.; Ammannito, E.; Giardino, M.; D'Aversa, E.; Fonte, S.; Tosi, F.

    2016-12-01

    Remote sensing images are generally affected by different types of noise that degrade the quality of the spectral data (i.e., stripes and spikes). Hyperspectral images returned by a Visible and InfraRed (VIR) spectrometer onboard the NASA Dawn mission exhibit residual systematic artifacts. VIR is an imaging spectrometer coupling high spectral and spatial resolutions in the visible and infrared spectral domain (0.25-5.0 μm). VIR data present one type of noise that may mask or distort real features (i.e., spikes and stripes), which may lead to misinterpretation of the surface composition. This paper presents a technique for the minimization of artifacts in VIR data that include a new instrument response function combining ground and in-flight radiometric measurements, correction of spectral spikes, odd-even band effects, systematic vertical stripes, high-frequency noise, and comparison with ground telescopic spectra of Vesta and Ceres. We developed a correction of artifacts in a two steps process: creation of the artifacts matrix and application of the same matrix to the VIR dataset. In the approach presented here, a polynomial function is used to fit the high frequency variations. After applying these corrections, the resulting spectra show improvements of the quality of the data. The new calibrated data enhance the significance of results from the spectral analysis of Vesta and Ceres.

  8. Kinematic artifacts in prestack depth migration.

    NARCIS (Netherlands)

    Stolk, C.C.; Symes, W.W.

    2004-01-01

    Strong refraction of waves in the migration velocity model introduces kinematic artifacts¿coherent events not corresponding to actual reflectors¿into the image volumes produced by prestack depth migration applied to individual data bins. Because individual bins are migrated independently, the migrat

  9. A constructivist approach to artifact development

    DEFF Research Database (Denmark)

    Skytte, Hans

    2008-01-01

    . The main result of the study shows that the concepts used (identity, image, organizational field etc.) to analyze the companies construct of the concepts, are linked in recursive patterns. This means that a company's artifact development takes place in recursive patterns consisting of concepts, meanings...

  10. Information Design for Visualizing History Museum Artifacts

    Science.gov (United States)

    Chen, Yulin; Lai, Tingsheng; Yasuda, Takami; Yokoi, Shigeki

    2011-01-01

    In the past few years, museum visualization systems have become a hot topic that attracts many researchers' interests. Several systems provide Web services for browsing museum collections through the Web. In this paper, we proposed an intelligent museum system for history museum artifacts, and described a study in which we enable access to China…

  11. Refining ensembles of predicted gene regulatory networks based on characteristic interaction sets.

    Directory of Open Access Journals (Sweden)

    Lukas Windhager

    Full Text Available Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate

  12. Model of Image Artifacts from Dust Particles

    Science.gov (United States)

    Willson, Reg

    2008-01-01

    A mathematical model of image artifacts produced by dust particles on lenses has been derived. Machine-vision systems often have to work with camera lenses that become dusty during use. Dust particles on the front surface of a lens produce image artifacts that can potentially affect the performance of a machine-vision algorithm. The present model satisfies a need for a means of synthesizing dust image artifacts for testing machine-vision algorithms for robustness (or the lack thereof) in the presence of dust on lenses. A dust particle can absorb light or scatter light out of some pixels, thereby giving rise to a dark dust artifact. It can also scatter light into other pixels, thereby giving rise to a bright dust artifact. For the sake of simplicity, this model deals only with dark dust artifacts. The model effectively represents dark dust artifacts as an attenuation image consisting of an array of diffuse darkened spots centered at image locations corresponding to the locations of dust particles. The dust artifacts are computationally incorporated into a given test image by simply multiplying the brightness value of each pixel by a transmission factor that incorporates the factor of attenuation, by dust particles, of the light incident on that pixel. With respect to computation of the attenuation and transmission factors, the model is based on a first-order geometric (ray)-optics treatment of the shadows cast by dust particles on the image detector. In this model, the light collected by a pixel is deemed to be confined to a pair of cones defined by the location of the pixel s image in object space, the entrance pupil of the lens, and the location of the pixel in the image plane (see Figure 1). For simplicity, it is assumed that the size of a dust particle is somewhat less than the diameter, at the front surface of the lens, of any collection cone containing all or part of that dust particle. Under this assumption, the shape of any individual dust particle artifact

  13. Artifacts for Calibration of Submicron Width Measurements

    Science.gov (United States)

    Grunthaner, Frank; Grunthaner, Paula; Bryson, Charles, III

    2003-01-01

    Artifacts that are fabricated with the help of molecular-beam epitaxy (MBE) are undergoing development for use as dimensional calibration standards with submicron widths. Such standards are needed for calibrating instruments (principally, scanning electron microscopes and scanning probe microscopes) for measuring the widths of features in advanced integrated circuits. Dimensional calibration standards fabricated by an older process that involves lithography and etching of trenches in (110) surfaces of single-crystal silicon are generally reproducible to within dimensional tolerances of about 15 nm. It is anticipated that when the artifacts of the present type are fully developed, their critical dimensions will be reproducible to within 1 nm. These artifacts are expected to find increasing use in the semiconductor-device and integrated- circuit industries as the width tolerances on semiconductor devices shrink to a few nanometers during the next few years. Unlike in the older process, one does not rely on lithography and etching to define the critical dimensions. Instead, one relies on the inherent smoothness and flatness of MBE layers deposited under controlled conditions and defines the critical dimensions as the thicknesses of such layers. An artifact of the present type is fabricated in two stages (see figure): In the first stage, a multilayer epitaxial wafer is grown on a very flat substrate. In the second stage, the wafer is cleaved to expose the layers, then the exposed layers are differentially etched (taking advantage of large differences between the etch rates of the different epitaxial layer materials). The resulting structure includes narrow and well-defined trenches and a shelf with thicknesses determined by the thicknesses of the epitaxial layers from which they were etched. Eventually, it should be possible to add a third fabrication stage in which durable, electronically inert artifacts could be replicated in diamondlike carbon from a master made by

  14. Clustering Gene Expression Data Based on Predicted Differential Effects of G V Interaction

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

    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 G V (gene by variety)interaction using the adjusted unbiased prediction (AUP) method. The predicted G V 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.

  15. Mammographic artifacts on full-field digital mammography.

    Science.gov (United States)

    Choi, Jae Jeong; Kim, Sung Hun; Kang, Bong Joo; Choi, Byung Gil; Song, ByungJoo; Jung, Haijo

    2014-04-01

    This study investigates the incidence of full-field digital mammographic (FFDM) artifacts with three systems at two institutions and compares the artifacts between two detector types and two grid types. A total of 4,440 direct and 4,142 indirect FFDM images were reviewed by two radiologists, and artifacts were classified as patient related, hardware related, and software processing. The overall incidence of FFDM artifacts was 3.4% (292/8,582). Patient related artifacts (motion artifacts and skin line artifacts) were the most commonly detected types (1.7%). Underexposure among hardware related artifacts and high-density artifacts among software processing artifacts also were common (0.7 and 0.5%, respectively). These artifacts, specific to digital mammography, were more common with the direct detector type and the crossed air grid type than with the indirect type and linear grid type (p artifacts on FFDM were patient related, which might be controlled by the instruction of a patient and technologist. Underexposure and high-density artifacts were more common with direct detector and crossed air type of grid.

  16. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  17. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  18. Netter: re-ranking gene network inference predictions using structural network properties.

    Science.gov (United States)

    Ruyssinck, Joeri; Demeester, Piet; Dhaene, Tom; Saeys, Yvan

    2016-02-09

    Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology. However, the structural properties of the predicted network do not resemble those typical for a gene regulatory network, as most algorithms only take into account connections found in the data and do not include known graph properties in their inference process. This lowers the prediction accuracy of these methods, limiting their usability in practice. We propose a post-processing algorithm which is applicable to any confidence ranking of regulatory interactions obtained from a network inference method which can use, inter alia, graphlets and several graph-invariant properties to re-rank the links into a more accurate prediction. To demonstrate the potential of our approach, we re-rank predictions of six different state-of-the-art algorithms using three simple network properties as optimization criteria and show that Netter can improve the predictions made on both artificially generated data as well as the DREAM4 and DREAM5 benchmarks. Additionally, the DREAM5 E.coli. community prediction inferred from real expression data is further improved. Furthermore, Netter compares favorably to other post-processing algorithms and is not restricted to correlation-like predictions. Lastly, we demonstrate that the performance increase is robust for a wide range of parameter settings. Netter is available at http://bioinformatics.intec.ugent.be. Network inference from high-throughput data is a long-standing challenge. In this work, we present Netter, which can further refine network predictions based on a set of user-defined graph properties. Netter is a flexible system which can be applied in unison with any method producing a ranking from omics data. It can be tailored to specific prior

  19. Predictive gene signatures: molecular markers distinguishing colon adenomatous polyp and carcinoma.

    Science.gov (United States)

    Drew, Janice E; Farquharson, Andrew J; Mayer, Claus Dieter; Vase, Hollie F; Coates, Philip J; Steele, Robert J; Carey, Francis A

    2014-01-01

    Cancers exhibit abnormal molecular signatures associated with disease initiation and progression. Molecular signatures could improve cancer screening, detection, drug development and selection of appropriate drug therapies for individual patients. Typically only very small amounts of tissue are available from patients for analysis and biopsy samples exhibit broad heterogeneity that cannot be captured using a single marker. This report details application of an in-house custom designed GenomeLab System multiplex gene expression assay, the hCellMarkerPlex, to assess predictive gene signatures of normal, adenomatous polyp and carcinoma colon tissue using archived tissue bank material. The hCellMarkerPlex incorporates twenty-one gene markers: epithelial (EZR, KRT18, NOX1, SLC9A2), proliferation (PCNA, CCND1, MS4A12), differentiation (B4GANLT2, CDX1, CDX2), apoptotic (CASP3, NOX1, NTN1), fibroblast (FSP1, COL1A1), structural (ACTG2, CNN1, DES), gene transcription (HDAC1), stem cell (LGR5), endothelial (VWF) and mucin production (MUC2). Gene signatures distinguished normal, adenomatous polyp and carcinoma. Individual gene targets significantly contributing to molecular tissue types, classifier genes, were further characterised using real-time PCR, in-situ hybridisation and immunohistochemistry revealing aberrant epithelial expression of MS4A12, LGR5 CDX2, NOX1 and SLC9A2 prior to development of carcinoma. Identified gene signatures identify aberrant epithelial expression of genes prior to cancer development using in-house custom designed gene expression multiplex assays. This approach may be used to assist in objective classification of disease initiation, staging, progression and therapeutic responses using biopsy material.

  20. Predictive gene signatures: molecular markers distinguishing colon adenomatous polyp and carcinoma.

    Directory of Open Access Journals (Sweden)

    Janice E Drew

    Full Text Available Cancers exhibit abnormal molecular signatures associated with disease initiation and progression. Molecular signatures could improve cancer screening, detection, drug development and selection of appropriate drug therapies for individual patients. Typically only very small amounts of tissue are available from patients for analysis and biopsy samples exhibit broad heterogeneity that cannot be captured using a single marker. This report details application of an in-house custom designed GenomeLab System multiplex gene expression assay, the hCellMarkerPlex, to assess predictive gene signatures of normal, adenomatous polyp and carcinoma colon tissue using archived tissue bank material. The hCellMarkerPlex incorporates twenty-one gene markers: epithelial (EZR, KRT18, NOX1, SLC9A2, proliferation (PCNA, CCND1, MS4A12, differentiation (B4GANLT2, CDX1, CDX2, apoptotic (CASP3, NOX1, NTN1, fibroblast (FSP1, COL1A1, structural (ACTG2, CNN1, DES, gene transcription (HDAC1, stem cell (LGR5, endothelial (VWF and mucin production (MUC2. Gene signatures distinguished normal, adenomatous polyp and carcinoma. Individual gene targets significantly contributing to molecular tissue types, classifier genes, were further characterised using real-time PCR, in-situ hybridisation and immunohistochemistry revealing aberrant epithelial expression of MS4A12, LGR5 CDX2, NOX1 and SLC9A2 prior to development of carcinoma. Identified gene signatures identify aberrant epithelial expression of genes prior to cancer development using in-house custom designed gene expression multiplex assays. This approach may be used to assist in objective classification of disease initiation, staging, progression and therapeutic responses using biopsy material.

  1. Predictive gene lists for breast cancer prognosis: A topographic visualisation study

    Directory of Open Access Journals (Sweden)

    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

  2. A sputum gene expression signature predicts oral corticosteroid response in asthma.

    Science.gov (United States)

    Berthon, Bronwyn S; Gibson, Peter G; Wood, Lisa G; MacDonald-Wicks, Lesley K; Baines, Katherine J

    2017-06-01

    Biomarkers that predict responses to oral corticosteroids (OCS) facilitate patient selection for asthma treatment. We hypothesised that asthma patients would respond differently to OCS therapy, with biomarkers and inflammometry predicting response.Adults with stable asthma underwent a randomised controlled cross-over trial of 50 mg prednisolone daily for 10 days (n=55). A six-gene expression biomarker signature (CLC, CPA3, DNASE1L3, IL1B, ALPL and CXCR2) in induced sputum, and eosinophils in blood and sputum were assessed and predictors of response were investigated (changes in forced expiratory volume in 1 s (ΔFEV1), six-item Asthma Control Questionnaire score (ΔACQ6) or exhaled nitric oxide fraction (ΔFeNO)).At baseline, responders to OCS (n=25) had upregulated mast cell CPA3 gene expression, poorer lung function, and higher sputum and blood eosinophils. Following treatment, CLC and CPA3 gene expression was reduced, whereas DNASE1L3, IL1B, ALPL and CXCR2 expression remained unchanged. Receiver operating characteristic (ROC) analysis showed the six-gene expression biomarker signature as a better predictor of clinically significant responses to OCS than blood and sputum eosinophils.The six-gene expression signature including eosinophil and Th2 related mast cell biomarkers showed greater precision in predicting OCS response in stable asthma. Thus, a novel sputum gene expression signature highlights an additional role of mast cells in asthma, and could be a useful measurement to guide OCS therapy in asthma. Copyright ©ERS 2017.

  3. In vitro gene regulatory networks predict in vivo function of liver

    Directory of Open Access Journals (Sweden)

    Ang Choo Y

    2010-11-01

    Full Text Available Abstract Background Evolution of toxicity testing is predicated upon using in vitro cell based systems to rapidly screen and predict how a chemical might cause toxicity to an organ in vivo. However, the degree to which we can extend in vitro results to in vivo activity and possible mechanisms of action remains to be fully addressed. Results Here we use the nitroaromatic 2,4,6-trinitrotoluene (TNT as a model chemical to compare and determine how we might extrapolate from in vitro data to in vivo effects. We found 341 transcripts differentially expressed in common among in vitro and in vivo assays in response to TNT. The major functional term corresponding to these transcripts was cell cycle. Similarly modulated common pathways were identified between in vitro and in vivo. Furthermore, we uncovered the conserved common transcriptional gene regulatory networks between in vitro and in vivo cellular liver systems that responded to TNT exposure, which mainly contain 2 subnetwork modules: PTTG1 and PIR centered networks. Interestingly, all 7 genes in the PTTG1 module were involved in cell cycle and downregulated by TNT both in vitro and in vivo. Conclusions The results of our investigation of TNT effects on gene expression in liver suggest that gene regulatory networks obtained from an in vitro system can predict in vivo function and mechanisms. Inhibiting PTTG1 and its targeted cell cyle related genes could be key machanism for TNT induced liver toxicity.

  4. In vitro gene regulatory networks predict in vivo function of liver

    Science.gov (United States)

    2010-01-01

    Background Evolution of toxicity testing is predicated upon using in vitro cell based systems to rapidly screen and predict how a chemical might cause toxicity to an organ in vivo. However, the degree to which we can extend in vitro results to in vivo activity and possible mechanisms of action remains to be fully addressed. Results Here we use the nitroaromatic 2,4,6-trinitrotoluene (TNT) as a model chemical to compare and determine how we might extrapolate from in vitro data to in vivo effects. We found 341 transcripts differentially expressed in common among in vitro and in vivo assays in response to TNT. The major functional term corresponding to these transcripts was cell cycle. Similarly modulated common pathways were identified between in vitro and in vivo. Furthermore, we uncovered the conserved common transcriptional gene regulatory networks between in vitro and in vivo cellular liver systems that responded to TNT exposure, which mainly contain 2 subnetwork modules: PTTG1 and PIR centered networks. Interestingly, all 7 genes in the PTTG1 module were involved in cell cycle and downregulated by TNT both in vitro and in vivo. Conclusions The results of our investigation of TNT effects on gene expression in liver suggest that gene regulatory networks obtained from an in vitro system can predict in vivo function and mechanisms. Inhibiting PTTG1 and its targeted cell cyle related genes could be key machanism for TNT induced liver toxicity. PMID:21073692

  5. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    Science.gov (United States)

    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.

  6. A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

    Directory of Open Access Journals (Sweden)

    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.

  7. A regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data.

    Science.gov (United States)

    Yao, Zizhen; Ruzzo, Walter L

    2006-03-20

    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. 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. 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 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.

  8. Genomic prediction contributing to a promising global strategy to turbocharge gene banks.

    Science.gov (United States)

    Yu, Xiaoqing; Li, Xianran; Guo, Tingting; Zhu, Chengsong; Wu, Yuye; Mitchell, Sharon E; Roozeboom, Kraig L; Wang, Donghai; Wang, Ming Li; Pederson, Gary A; Tesso, Tesfaye T; Schnable, Patrick S; Bernardo, Rex; Yu, Jianming

    2016-10-03

    The 7.4 million plant accessions in gene banks are largely underutilized due to various resource constraints, but current genomic and analytic technologies are enabling us to mine this natural heritage. Here we report a proof-of-concept study to integrate genomic prediction into a broad germplasm evaluation process. First, a set of 962 biomass sorghum accessions were chosen as a reference set by germplasm curators. With high throughput genotyping-by-sequencing (GBS), we genetically characterized this reference set with 340,496 single nucleotide polymorphisms (SNPs). A set of 299 accessions was selected as the training set to represent the overall diversity of the reference set, and we phenotypically characterized the training set for biomass yield and other related traits. Cross-validation with multiple analytical methods using the data of this training set indicated high prediction accuracy for biomass yield. Empirical experiments with a 200-accession validation set chosen from the reference set confirmed high prediction accuracy. The potential to apply the prediction model to broader genetic contexts was also examined with an independent population. Detailed analyses on prediction reliability provided new insights into strategy optimization. The success of this project illustrates that a global, cost-effective strategy may be designed to assess the vast amount of valuable germplasm archived in 1,750 gene banks.

  9. Smoking Artifacts as Indicators of Homophily, Attraction, and Credibility.

    Science.gov (United States)

    Hickson, Mark, III; And Others

    1979-01-01

    Describes a study of the influence of smoking artifacts on the perceptions of a source's homophily, interpersonal attraction, and credibility. Significant differences were found based upon the type of smoking artifact used and the sex of the subject. (JMF)

  10. Artifacts and pitfalls of high-resolution CT scans.

    Science.gov (United States)

    Hahn, F J; Chu, W K; Anderson, J C; Dobry, C A

    1985-01-01

    Artifacts on CT images have been observed since the introduction of CT scanners. Some artifacts have been corrected with the improvement of technology and better understanding of the image formation and reconstruction algorithms. Some artifacts, however, are still observable in state-of-the-art high-resolution scans. Many investigations on CT artifacts have been reported. Some artifacts are obvious and some are similar to patterns commonly associated with pathological conditions. The present report summarizes some of the causes of artifacts and presents some artifacts that mimic pathology on clinical scans of the head and spine. It is the intention of this report to bring these artifacts and potential pitfalls to the attention of the radiologists so that misinterpretation can be avoided.

  11. Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Pingzhao Hu

    2006-01-01

    biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer. Conclusions: Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer.

  12. Gene expression profiling to predict the risk of locoregional recurrence in breast cancer: a pooled analysis.

    Science.gov (United States)

    Drukker, C A; Elias, S G; Nijenhuis, M V; Wesseling, J; Bartelink, H; Elkhuizen, P; Fowble, B; Whitworth, P W; Patel, R R; de Snoo, F A; van 't Veer, L J; Beitsch, P D; Rutgers, E J Th

    2014-12-01

    The 70-gene signature (MammaPrint) has been developed to predict the risk of distant metastases in breast cancer and select those patients who may benefit from adjuvant treatment. Given the strong association between locoregional and distant recurrence, we hypothesize that the 70-gene signature will also be able to predict the risk of locoregional recurrence (LRR). 1,053 breast cancer patients primarily treated with breast-conserving treatment or mastectomy at the Netherlands Cancer Institute between 1984 and 2006 were included. Adjuvant treatment consisted of radiotherapy, chemotherapy, and/or endocrine therapy as indicated by guidelines used at the time. All patients were included in various 70-gene signature validation studies. After a median follow-up of 8.96 years with 87 LRRs, patients with a high-risk 70-gene signature (n = 492) had an LRR risk of 12.6% (95% CI 9.7-15.8) at 10 years, compared to 6.1% (95% CI 4.1-8.5) for low-risk patients (n = 561; P risk model for the clinicopathological factors such as age, tumour size, grade, hormone receptor status, LVI, axillary lymph node involvement, surgical treatment, endocrine treatment, and chemotherapy resulted in a multivariable HR of 1.73 (95% CI 1.02-2.93; P = 0.042). Adding the signature to the model based on clinicopathological factors improved the discrimination, albeit non-significantly [C-index through 10 years changed from 0.731 (95% CI 0.682-0.782) to 0.741 (95% CI 0.693-0.790)]. Calibration of the prognostic models was excellent. The 70-gene signature is an independent prognostic factor for LRR. A significantly lower local recurrence risk was seen in patients with a low-risk 70-gene signature compared to those with high-risk 70-gene signature.

  13. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen.

    Science.gov (United States)

    Ma, Xiao-Jun; Wang, Zuncai; Ryan, Paula D; Isakoff, Steven J; Barmettler, Anne; Fuller, Andrew; Muir, Beth; Mohapatra, Gayatry; Salunga, Ranelle; Tuggle, J Todd; Tran, Yen; Tran, Diem; Tassin, Ana; Amon, Paul; Wang, Wilson; Wang, Wei; Enright, Edward; Stecker, Kimberly; Estepa-Sabal, Eden; Smith, Barbara; Younger, Jerry; Balis, Ulysses; Michaelson, James; Bhan, Atul; Habin, Karleen; Baer, Thomas M; Brugge, Joan; Haber, Daniel A; Erlander, Mark G; Sgroi, Dennis C

    2004-06-01

    Tamoxifen significantly reduces tumor recurrence in certain patients with early-stage estrogen receptor-positive breast cancer, but markers predictive of treatment failure have not been identified. Here, we generated gene expression profiles of hormone receptor-positive primary breast cancers in a set of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a two-gene ratio, HOXB13 versus IL17BR, which outperformed existing biomarkers. Ectopic expression of HOXB13 in MCF10A breast epithelial cells enhances motility and invasion in vitro, and its expression is increased in both preinvasive and invasive primary breast cancer. The HOXB13:IL17BR expression ratio may be useful for identifying patients appropriate for alternative therapeutic regimens in early-stage breast cancer.

  14. K-space model of motion artifacts in synthetic transmit aperture ultrasound imaging

    DEFF Research Database (Denmark)

    Nikolov, Svetoslav; Jensen, Jørgen Arendt

    2003-01-01

    and leads to distortions in the image. In order to develop motion compensation and/or velocity estimation algorithms a thorough and intuitive understanding of the nature of motion artifacts is needed. This paper proposes a simple 2D broad band model for STA images, based on the acquisition procedure...... resolution image as a sum of rotated PSFs of a single LRI. The approximation is validated with a Field II simulation. The model predicts and explains the motion artifacts, and gives an intuitive feeling of what would happen for different velocities....

  15. A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions

    Science.gov (United States)

    Creighton, Chad J.; Nagaraja, Ankur K.; Hanash, Samir M.; Matzuk, Martin M.; Gunaratne, Preethi H.

    2008-01-01

    MicroRNAs are short (∼22 nucleotides) noncoding RNAs that regulate the stability and translation of mRNA targets. A number of computational algorithms have been developed to help predict which microRNAs are likely to regulate which genes. Gene expression profiling of biological systems where microRNAs might be active can yield hundreds of differentially expressed genes. The commonly used public microRNA target prediction databases facilitate gene-by-gene searches. However, integration of microRNA–mRNA target predictions with gene expression data on a large scale using these databases is currently cumbersome and time consuming for many researchers. We have developed a desktop software application which, for a given target prediction database, retrieves all microRNA:mRNA functional pairs represented by an experimentally derived set of genes. Furthermore, for each microRNA, the software computes an enrichment statistic for overrepresentation of predicted targets within the gene set, which could help to implicate roles for specific microRNAs and microRNA-regulated genes in the system under study. Currently, the software supports searching of results from PicTar, TargetScan, and miRanda algorithms. In addition, the software can accept any user-defined set of gene-to-class associations for searching, which can include the results of other target prediction algorithms, as well as gene annotation or gene-to-pathway associations. A search (using our software) of genes transcriptionally regulated in vitro by estrogen in breast cancer uncovered numerous targeting associations for specific microRNAs—above what could be observed in randomly generated gene lists—suggesting a role for microRNAs in mediating the estrogen response. The software and Excel VBA source code are freely available at http://sigterms.sourceforge.net. PMID:18812437

  16. A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions.

    Science.gov (United States)

    Creighton, Chad J; Nagaraja, Ankur K; Hanash, Samir M; Matzuk, Martin M; Gunaratne, Preethi H

    2008-11-01

    MicroRNAs are short (approximately 22 nucleotides) noncoding RNAs that regulate the stability and translation of mRNA targets. A number of computational algorithms have been developed to help predict which microRNAs are likely to regulate which genes. Gene expression profiling of biological systems where microRNAs might be active can yield hundreds of differentially expressed genes. The commonly used public microRNA target prediction databases facilitate gene-by-gene searches. However, integration of microRNA-mRNA target predictions with gene expression data on a large scale using these databases is currently cumbersome and time consuming for many researchers. We have developed a desktop software application which, for a given target prediction database, retrieves all microRNA:mRNA functional pairs represented by an experimentally derived set of genes. Furthermore, for each microRNA, the software computes an enrichment statistic for overrepresentation of predicted targets within the gene set, which could help to implicate roles for specific microRNAs and microRNA-regulated genes in the system under study. Currently, the software supports searching of results from PicTar, TargetScan, and miRanda algorithms. In addition, the software can accept any user-defined set of gene-to-class associations for searching, which can include the results of other target prediction algorithms, as well as gene annotation or gene-to-pathway associations. A search (using our software) of genes transcriptionally regulated in vitro by estrogen in breast cancer uncovered numerous targeting associations for specific microRNAs-above what could be observed in randomly generated gene lists-suggesting a role for microRNAs in mediating the estrogen response. The software and Excel VBA source code are freely available at http://sigterms.sourceforge.net.

  17. Dynamics of the Transcriptome during Human Spermatogenesis: Predicting the Potential Key Genes Regulating Male Gametes Generation.

    Science.gov (United States)

    Zhu, Zijue; Li, Chong; Yang, Shi; Tian, Ruhui; Wang, Junlong; Yuan, Qingqing; Dong, Hui; He, Zuping; Wang, Shengyue; Li, Zheng

    2016-01-12

    Many infertile men are the victims of spermatogenesis disorder. However, conventional clinical test could not provide efficient information on the causes of spermatogenesis disorder and guide the doctor how to treat it. More effective diagnosis and treating methods could be developed if the key genes that regulate spermatogenesis were determined. Many works have been done on animal models, while there are few works on human beings due to the limited sample resources. In current work, testis tissues were obtained from 27 patients with obstructive azoospermia via surgery. The combination of Fluorescence Activated Cell Sorting and Magnetic Activated Cell Sorting was chosen as the efficient method to sort typical germ cells during spermatogenesis. RNA Sequencing was carried out to screen the change of transcriptomic profile of the germ cells during spermatogenesis. Differential expressed genes were clustered according to their expression patterns. Gene Ontology annotation, pathway analysis, and Gene Set Enrichment Analysis were carried out on genes with specific expression patterns and the potential key genes such as HOXs, JUN, SP1, and TCF3 which were involved in the regulation of spermatogenesis, with the potential value serve as molecular tools for clinical purpose, were predicted.

  18. Racial IQ Differences among Transracial Adoptees: Fact or Artifact?

    Directory of Open Access Journals (Sweden)

    Drew Thomas

    2016-12-01

    Full Text Available Some academic publications infer from studies of transracial adoptees’ IQs that East Asian adoptees raised in the West by Whites have higher IQs than Western Whites, and that White adoptees raised by Whites have higher IQs than Black adoptees raised by Whites. Those publications suggest that this is because genetic differences give East Asians a higher mean IQ than Whites, and Whites a higher mean IQ than Blacks. This paper proposes a parsimonious alternative explanation: the apparent IQ advantage of East Asian adoptees is an artifact caused by ignoring the Flynn effect and adoption’s beneficial effect on IQ, and most of the IQ disadvantage of Black adoptees disappears when one allows for attrition in the Minnesota Transracial Adoption Study, and acknowledges the results of other studies. Diagnosing these artifacts suggests a nil hypothesis: East Asian, White, and Black adoptees raised in the same environment would have similar IQs, hinting at a minimal role for genes in racial IQ differences.

  19. Teaching and Learning the Nature of Technical Artifacts

    Science.gov (United States)

    Frederik, Ineke; Sonneveld, Wim; de Vries, Marc J.

    2011-01-01

    Artifacts are probably our most obvious everyday encounter with technology. Therefore, a good understanding of the nature of technical artifacts is a relevant part of technological literacy. In this article we draw from the philosophy of technology to develop a conceptualization of technical artifacts that can be used for educational purposes.…

  20. Connecting Student and Subject Matter: The Cultural Artifact Discussion Assignment

    Science.gov (United States)

    Smith-Sanders, Alane K.

    2008-01-01

    This article presents a class activity where students work in dyads to select an artifact related to a course topic and, using this artifact, develop discussion questions to engage their classmates. This cultural artifact assignment is intended to, in part, answer John Dewey's call to cultivate connections between subject matter and life…

  1. Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data

    Directory of Open Access Journals (Sweden)

    Kim Seon-Young

    2006-07-01

    Full Text Available Abstract Background A complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast. Results We report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis. Conclusion By a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding

  2. Accuracy and artifact: reexamining the intensity bias in affective forecasting.

    Science.gov (United States)

    Levine, Linda J; Lench, Heather C; Kaplan, Robin L; Safer, Martin A

    2012-10-01

    Research on affective forecasting shows that people have a robust tendency to overestimate the intensity of future emotion. We hypothesized that (a) people can accurately predict the intensity of their feelings about events and (b) a procedural artifact contributes to people's tendency to overestimate the intensity of their feelings in general. People may misinterpret the forecasting question as asking how they will feel about a focal event, but they are later asked to report their feelings in general without reference to that event. In the current investigation, participants predicted and reported both their feelings in general and their feelings about an election outcome (Study 1) and an exam grade (Study 3). We also assessed how participants interpreted forecasting questions (Studies 2 and 4) and conducted a meta-analysis of affective forecasting research (Study 5). The results showed that participants accurately predicted the intensity of their feelings about events. They overestimated only when asked to predict how they would feel in general and later report their feelings without reference to the focal event. Most participants, however, misinterpreted requests to predict their feelings in general as asking how they would feel when they were thinking about the focal event. Clarifying the meaning of the forecasting question significantly reduced overestimation. These findings reveal that people have more sophisticated self-knowledge than is commonly portrayed in the affective forecasting literature. Overestimation of future emotion is partly due to a procedure in which people predict one thing but are later asked to report another.

  3. antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification

    DEFF Research Database (Denmark)

    Blin, Kai; Wolf, Thomas; Chevrette, Marc G.

    2017-01-01

    Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the product......Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding...... the production of such compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features......, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally...

  4. Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays

    Directory of Open Access Journals (Sweden)

    Aramburu Ander

    2010-11-01

    Full Text Available Abstract Background Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them. The reason is that previously developed summarization methods do not work well if applied to transcripts. In addition, gene structure prediction, i.e., the correspondence between probes and novel isoforms, is a field which is still unexplored. Results We have modified and adapted a previous algorithm to take advantage of the special characteristics of the Affymetrix exon arrays. The structure and concentration of transcripts -some of them possibly unknown- in microarray experiments were predicted using this algorithm. Simulations showed that the suggested modifications improved both specificity (SP and sensitivity (ST of the predictions. The algorithm was also applied to different real datasets showing its effectiveness and the concordance with PCR validated results. Conclusions The proposed algorithm shows a substantial improvement in the performance over the previous version. This improvement is mainly due to the exploitation of the redundancy of the Affymetrix exon arrays. An R-Package of SPACE with the updated algorithms have been developed and is freely available.

  5. Development and Validation of Predictive Indices for a Continuous Outcome Using Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Yingdong Zhao

    2010-05-01

    Full Text Available There have been relatively few publications using linear regression models to predict a continuous response based on microarray expression profiles. Standard linear regression methods are problematic when the number of predictor variables exceeds the number of cases. We have evaluated three linear regression algorithms that can be used for the prediction of a continuous response based on high dimensional gene expression data. The three algorithms are the least angle regression (LAR, the least absolute shrinkage and selection operator (LASSO, and the averaged linear regression method (ALM. All methods are tested using simulations based on a real gene expression dataset and analyses of two sets of real gene expression data and using an unbiased complete cross validation approach. Our results show that the LASSO algorithm often provides a model with somewhat lower prediction error than the LAR method, but both of them perform more efficiently than the ALM predictor. We have developed a plug-in for BRB-ArrayTools that implements the LAR and the LASSO algorithms with complete cross-validation.

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

    Indian Academy of Sciences (India)

    Nihir Patel; T L Wang

    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.

  7. Use of tiling array data and RNA secondary structure predictions to identify noncoding RNA genes

    DEFF Research Database (Denmark)

    Weile, Christian; Gardner, Paul P; Hedegaard, Mads M

    2007-01-01

    BACKGROUND: Within the last decade a large number of noncoding RNA genes have been identified, but this may only be the tip of the iceberg. Using comparative genomics a large number of sequences that have signals concordant with conserved RNA secondary structures have been discovered in the human...... genome. Moreover, genome wide transcription profiling with tiling arrays indicate that the majority of the genome is transcribed. RESULTS: We have combined tiling array data with genome wide structural RNA predictions to search for novel noncoding and structural RNA genes that are expressed in the human...... of 3 of the hairpin structures and 3 out of 9 high covariance structures in SK-N-AS cells. CONCLUSION: Our results demonstrate that many human noncoding, structured and conserved RNA genes remain to be discovered and that tissue specific tiling array data can be used in combination with computational...

  8. Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

    Science.gov (United States)

    Shedden, Kerby; Taylor, Jeremy M.G.; Enkemann, Steve A.; Tsao, Ming S.; Yeatman, Timothy J.; Gerald, William L.; Eschrich, Steve; Jurisica, Igor; Venkatraman, Seshan E.; Meyerson, Matthew; Kuick, Rork; Dobbin, Kevin K.; Lively, Tracy; Jacobson, James W.; Beer, David G.; Giordano, Thomas J.; Misek, David E.; Chang, Andrew C.; Zhu, Chang Qi; Strumpf, Dan; Hanash, Samir; Shepherd, Francis A.; Ding, Kuyue; Seymour, Lesley; Naoki, Katsuhiko; Pennell, Nathan; Weir, Barbara; Verhaak, Roel; Ladd-Acosta, Christine; Golub, Todd; Gruidl, Mike; Szoke, Janos; Zakowski, Maureen; Rusch, Valerie; Kris, Mark; Viale, Agnes; Motoi, Noriko; Travis, William; Sharma, Anupama

    2009-01-01

    Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. PMID:18641660

  9. Reassessing Domain Architecture Evolution of Metazoan Proteins: Major Impact of Gene Prediction Errors

    Directory of Open Access Journals (Sweden)

    László Patthy

    2011-07-01

    Full Text Available In view of the fact that appearance of novel protein domain architectures (DA is closely associated with biological innovations, there is a growing interest in the genome-scale reconstruction of the evolutionary history of the domain architectures of multidomain proteins. In such analyses, however, it is usually ignored that a significant proportion of Metazoan sequences analyzed is mispredicted and that this may seriously affect the validity of the conclusions. To estimate the contribution of errors in gene prediction to differences in DA of predicted proteins, we have used the high quality manually curated UniProtKB/Swiss-Prot database as a reference. For genome-scale analysis of domain architectures of predicted proteins we focused on RefSeq, EnsEMBL and NCBI’s GNOMON predicted sequences of Metazoan species with completely sequenced genomes. Comparison of the DA of UniProtKB/Swiss-Prot sequences of worm, fly, zebrafish, frog, chick, mouse, rat and orangutan with those of human Swiss-Prot entries have identified relatively few cases where orthologs had different DA, although the percentage with different DA increased with evolutionary distance. In contrast with this, comparison of the DA of human, orangutan, rat, mouse, chicken, frog, zebrafish, worm and fly RefSeq, EnsEMBL and NCBI’s GNOMON predicted protein sequences with those of the corresponding/orthologous human Swiss-Prot entries identified a significantly higher proportion of domain architecture differences than in the case of the comparison of Swiss-Prot entries. Analysis of RefSeq, EnsEMBL and NCBI’s GNOMON predicted protein sequences with DAs different from those of their Swiss-Prot orthologs confirmed that the higher rate of domain architecture differences is due to errors in gene prediction, the majority of which could be corrected with our FixPred protocol. We have also demonstrated that contamination of databases with incomplete, abnormal or mispredicted sequences

  10. Interactome of Radiation-Induced microRNA-Predicted Target Genes

    Directory of Open Access Journals (Sweden)

    Tenzin W. Lhakhang

    2012-01-01

    Full Text Available The microRNAs (miRNAs function as global negative regulators of gene expression and have been associated with a multitude of biological processes. The dysfunction of the microRNAome has been linked to various diseases including cancer. Our laboratory recently reported modulation in the expression of miRNA in a variety of cell types exposed to ionizing radiation (IR. To further understand miRNA role in IR-induced stress pathways, we catalogued a set of common miRNAs modulated in various irradiated cell lines and generated a list of predicted target genes. Using advanced bioinformatics tools we identified cellular pathways where miRNA predicted target genes function. The miRNA-targeted genes were found to play key roles in previously identified IR stress pathways such as cell cycle, p53 pathway, TGF-beta pathway, ubiquitin-mediated proteolysis, focal adhesion pathway, MAPK signaling, thyroid cancer pathway, adherens junction, insulin signaling pathway, oocyte meiosis, regulation of actin cytoskeleton, and renal cell carcinoma pathway. Interestingly, we were able to identify novel targeted pathways that have not been identified in cellular radiation response, such as aldosterone-regulated sodium reabsorption, long-term potentiation, and neutrotrophin signaling pathways. Our analysis indicates that the miRNA interactome in irradiated cells provides a platform for comprehensive modeling of the cellular stress response to IR exposure.

  11. Supporting Knowledge Transfer through Decomposable Reasoning Artifacts

    Energy Technology Data Exchange (ETDEWEB)

    Pike, William A.; May, Richard A.; Turner, Alan E.

    2007-01-03

    Technology to support knowledge transfer and cooperative inquiry must offer its users the ability to effectively interpret knowledge structures produced by collaborators. Communicating the reasoning processes that underlie a finding is one method for enhancing interpretation, and can result in more effective evaluation and application of shared knowledge. In knowledge management tools, interpretation is aided by creating knowledge artifacts that can expose their provenance to scrutiny and that can be transformed into diverse representations that suit their consumers’ perspectives and preferences. We outline the information management needs of inquiring communities characterized by hypothesis generation tasks, and propose a model for communication, based in theories of hermeneutics, semiotics, and abduction, in which knowledge structures can be decomposed into the lower-level reasoning artifacts that produced them. We then present a proof-of-concept implementation for an environment to support the capture and communication of analytic products, with emphasis on the domain of intelligence analysis.

  12. Social web artifacts for boosting recommenders theory and implementation

    CERN Document Server

    Ziegler, Cai-Nicolas

    2013-01-01

    Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the “Web 2.0” or the “Social Web”: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to impr...

  13. Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.

    Directory of Open Access Journals (Sweden)

    Ying-Wooi Wan

    Full Text Available Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7 and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4 and DFCI (n = 82; log-rank P = 2.57e-4, using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04. The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46] than other commonly used clinical factors except tumor stage (III vs. I in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017. The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of

  14. iFish: predicting the pathogenicity of human nonsynonymous variants using gene-specific/family-specific attributes and classifiers.

    Science.gov (United States)

    Wang, Meng; Wei, Liping

    2016-08-16

    Accurate prediction of the pathogenicity of genomic variants, especially nonsynonymous single nucleotide variants (nsSNVs), is essential in biomedical research and clinical genetics. Most current prediction methods build a generic classifier for all genes. However, different genes and gene families have different features. We investigated whether gene-specific and family-specific customized classifiers could improve prediction accuracy. Customized gene-specific and family-specific attributes were selected with AIC, BIC, and LASSO, and Support Vector Machine classifiers were generated for 254 genes and 152 gene families, covering a total of 5,985 genes. Our results showed that the customized attributes reflected key features of the genes and gene families, and the customized classifiers achieved higher prediction accuracy than the generic classifier. The customized classifiers and the generic classifier for other genes and families were integrated into a new tool named iFish (integrated Functional inference of SNVs in human, http://ifish.cbi.pku.edu.cn). iFish outperformed other methods on benchmark datasets as well as on prioritization of candidate causal variants from whole exome sequencing. iFish provides a user-friendly web-based interface and supports other functionalities such as integration of genetic evidence. iFish would facilitate high-throughput evaluation and prioritization of nsSNVs in human genetics research.

  15. Artifacts Of Spectral Analysis Of Instrument Readings

    Science.gov (United States)

    Wise, James H.

    1995-01-01

    Report presents experimental and theoretical study of some of artifacts introduced by processing outputs of two nominally identical low-frequency-reading instruments; high-sensitivity servo-accelerometers mounted together and operating, in conjunction with signal-conditioning circuits, as seismometers. Processing involved analog-to-digital conversion with anti-aliasing filtering, followed by digital processing including frequency weighting and computation of different measures of power spectral density (PSD).

  16. [Hybrid interpolation for CT metal artifact reducing].

    Science.gov (United States)

    Yu, Xiao-e; Li, Chan-juan; Chen, Wu-fan

    2009-01-01

    Numerous interpolation-based methods have been described for reducing metal artifacts in CT images, but due to the limit of the interpolation methods, interpolation alone often fails to meet the clinical demands. In this paper, we describe the use of quartic polynomial interpolation in reconstruction of the images of the metal implant followed by linear interpolation to eliminate the streaks. The two interpolation methods are combined according to their given weights to achieve good results.

  17. Panning artifacts in digital pathology images

    Science.gov (United States)

    Avanaki, Ali R. N.; Lanciault, Christian; Espig, Kathryn S.; Xthona, Albert; Kimpe, Tom R. L.

    2017-03-01

    In making a pathologic diagnosis, a pathologist uses cognitive processes: perception, attention, memory, and search (Pena and Andrade-Filho, 2009). Typically, this involves focus while panning from one region of a slide to another, using either a microscope in a traditional workflow or software program and display in a digital pathology workflow (DICOM Standard Committee, 2010). We theorize that during panning operation, the pathologist receives information important to diagnosis efficiency and/or correctness. As compared to an optical microscope, panning in a digital pathology image involves some visual artifacts due to the following: (i) the frame rate is finite; (ii) time varying visual signals are reconstructed using imperfect zero-order hold. Specifically, after pixel's digital drive is changed, it takes time for a pixel to emit the expected amount of light. Previous work suggests that 49% of navigation is conducted in low-power/overview with digital pathology (Molin et al., 2015), but the influence of display factors has not been measured. We conducted a reader study to establish a relationship between display frame rate, panel response time, and threshold panning speed (above which the artifacts become noticeable). Our results suggest visual tasks that involve tissue structure are more impacted by the simulated panning artifacts than those that only involve color (e.g., staining intensity estimation), and that the panning artifacts versus normalized panning speed has a peak behavior which is surprising and may change for a diagnostic task. This is work in progress and our final findings should be considered in designing future digital pathology systems.

  18. Predicting gene function using hierarchical multi-label decision tree ensembles

    Directory of Open Access Journals (Sweden)

    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.

  19. A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT

    Science.gov (United States)

    Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim

    2011-06-01

    Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.

  20. A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT

    Energy Technology Data Exchange (ETDEWEB)

    Fieselmann, Andreas; Hornegger, Joachim [Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen (Germany); Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan [Siemens AG, Healthcare Sector, Angiography and Interventional X-Ray Systems, Siemensstr. 1, 91301 Forchheim (Germany); Fahrig, Rebecca, E-mail: andreas.fieselmann@informatik.uni-erlangen.de [Department of Radiology, Lucas MRS Center, Stanford University, 1201 Welch Road, Palo Alto, CA 94305 (United States)

    2011-06-21

    Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.

  1. [A novel method of the genome-wide prediction for the target genes and its application].

    Science.gov (United States)

    Zhang, Jing-Jing; Feng, Jing; Zhu, Ying-Guo; Li, Yang-Sheng

    2006-10-01

    Based on the protein databases of several model species, this study developed a new method of the Genome-wide prediction for the target genes, using Hidden Markov model by Perl programming. The advantages of this method are high throughput, high quality and easy prediction, especially in the case of multi-domains proteins families. By this method, we predicted the PPR and TPR proteins families in whole genome of several model species. There were 536 PPR proteins and 199 TPR proteins in Oryza sativa ssp. japonica, 519 PPR proteins and 177 TPR proteins in Oryza sativa L. ssp. indica, 735 PPR proteins and 292 TPR proteins in Arabidopsis thaliana, 6 PPR proteins and 32 TPR proteins in Cyanidioschyzon merolae. Synechococcus and Thermophilic archaebacterium did not have PPR proteins. By contrast, 10 TPR proteins were found in Synechococcus and 4 TPR proteins were found in Thermophilic archaebacterium. Moreover, of these results, some further bioinformatics analyses were conducted.

  2. Sampling Artifacts from Conductive Silicone Tubing

    Energy Technology Data Exchange (ETDEWEB)

    Timko, Michael T.; Yu, Zhenhong; Kroll, Jesse; Jayne, John T.; Worsnop, Douglas R.; Miake-Lye, Richard C.; Onasch, Timothy B.; Liscinsky, David; Kirchstetter, Thomas W.; Destaillats, Hugo; Holder, Amara L.; Smith, Jared D.; Wilson, Kevin R.

    2009-05-15

    We report evidence that carbon impregnated conductive silicone tubing used in aerosol sampling systems can introduce two types of experimental artifacts: 1) silicon tubing dynamically absorbs carbon dioxide gas, requiring greater than 5 minutes to reach equilibrium and 2) silicone tubing emits organic contaminants containing siloxane that adsorb onto particles traveling through it and onto downstream quartz fiber filters. The consequence can be substantial for engine exhaust measurements as both artifacts directly impact calculations of particulate mass-based emission indices. The emission of contaminants from the silicone tubing can result in overestimation of organic particle mass concentrations based on real-time aerosol mass spectrometry and the off-line thermal analysis of quartz filters. The adsorption of siloxane contaminants can affect the surface properties of aerosol particles; we observed a marked reduction in the water-affinity of soot particles passed through conductive silicone tubing. These combined observations suggest that the silicone tubing artifacts may have wide consequence for the aerosol community and should, therefore, be used with caution. Gentle heating, physical and chemical properties of the particle carriers, exposure to solvents, and tubing age may influence siloxane uptake. The amount of contamination is expected to increase as the tubing surface area increases and as the particle surface area increases. The effect is observed at ambient temperature and enhanced by mild heating (<100 oC). Further evaluation is warranted.

  3. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve.

    Science.gov (United States)

    Goswami, C Pankaj; Cheng, L; Alexander, P S; Singal, A; Li, L

    2015-02-01

    Gene expression data before and after treatment with an individual drug and the IC20 of dose-response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database.

  4. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds.

    Directory of Open Access Journals (Sweden)

    Howard Y Chang

    2004-02-01

    Full Text Available Cancer invasion and metastasis have been likened to wound healing gone awry. Despite parallels in cellular behavior between cancer progression and wound healing, the molecular relationships between these two processes and their prognostic implications are unclear. In this study, based on gene expression profiles of fibroblasts from ten anatomic sites, we identify a stereotyped gene expression program in response to serum exposure that appears to reflect the multifaceted role of fibroblasts in wound healing. The genes comprising this fibroblast common serum response are coordinately regulated in many human tumors, allowing us to identify tumors with gene expression signatures suggestive of active wounds. Genes induced in the fibroblast serum-response program are expressed in tumors by the tumor cells themselves, by tumor-associated fibroblasts, or both. The molecular features that define this wound-like phenotype are evident at an early clinical stage, persist during treatment, and predict increased risk of metastasis and death in breast, lung, and gastric carcinomas. Thus, the transcriptional signature of the response of fibroblasts to serum provides a possible link between cancer progression and wound healing, as well as a powerful predictor of the clinical course in several common carcinomas.

  5. Four genes predict high risk of progression from smoldering to symptomatic multiple myeloma (SWOG S0120).

    Science.gov (United States)

    Khan, Rashid; Dhodapkar, Madhav; Rosenthal, Adam; Heuck, Christoph; Papanikolaou, Xenofon; Qu, Pingping; van Rhee, Frits; Zangari, Maurizio; Jethava, Yogesh; Epstein, Joshua; Yaccoby, Shmuel; Hoering, Antje; Crowley, John; Petty, Nathan; Bailey, Clyde; Morgan, Gareth; Barlogie, Bart

    2015-09-01

    Multiple myeloma is preceded by an asymptomatic phase, comprising monoclonal gammopathy of uncertain significance and smoldering myeloma. Compared to the former, smoldering myeloma has a higher and non-uniform rate of progression to clinical myeloma, reflecting a subset of patients with higher risk. We evaluated the gene expression profile of smoldering myeloma plasma cells among 105 patients enrolled in a prospective observational trial at our institution, with a view to identifying a high-risk signature. Baseline clinical, bone marrow, cytogenetic and radiologic data were evaluated for their potential to predict time to therapy for symptomatic myeloma. A gene signature derived from four genes, at an optimal binary cut-point of 9.28, identified 14 patients (13%) with a 2-year therapy risk of 85.7%. Conversely, a low four-gene score (probe sets showed concordance with indices of chromosome instability. These data demonstrate high discriminatory power of a gene-based assay and suggest a role for dysregulation of mitotic checkpoints in the context of genomic instability as a hallmark of high-risk smoldering myeloma.

  6. Indole-Diterpene Biosynthetic Capability of Epichloë Endophytes as Predicted by ltm Gene Analysis▿

    Science.gov (United States)

    Young, Carolyn A.; Tapper, Brian A.; May, Kimberley; Moon, Christina D.; Schardl, Christopher L.; Scott, Barry

    2009-01-01

    Bioprotective alkaloids produced by Epichloë and closely related asexual Neotyphodium fungal endophytes protect their grass hosts from insect and mammalian herbivory. One class of these compounds, known for antimammalian toxicity, is the indole-diterpenes. The LTM locus of Neotyphodium lolii (Lp19) and Epichloë festuce (Fl1), required for the biosynthesis of the indole-diterpene lolitrem, consists of 10 ltm genes. We have used PCR and Southern analysis to screen a broad taxonomic range of 44 endophyte isolates to determine why indole-diterpenes are present in so few endophyte-grass associations in comparison to that of the other bioprotective alkaloids, which are more widespread among the endophtyes. All 10 ltm genes were present in only three epichloë endophytes. A predominance of the asexual Neotyphodium spp. examined contained 8 of the 10 ltm genes, with only one N. lolii containing the entire LTM locus and the ability to produce lolitrems. Liquid chromatography-tandem mass spectrometry profiles of indole-diterpenes from a subset of endophyte-infected perennial ryegrass showed that endophytes that contained functional genes present in ltm clusters 1 and 2 were capable of producing simple indole-diterpenes such as paspaline, 13-desoxypaxilline, and terpendoles, compounds predicted to be precursors of lolitrem B. Analysis of toxin biosynthesis genes by PCR now enables a diagnostic method to screen endophytes for both beneficial and detrimental alkaloids and can be used as a resource for screening isolates required for forage improvement. PMID:19181837

  7. Voting strategy for artifact reduction in digital breast tomosynthesis.

    Science.gov (United States)

    Wu, Tao; Moore, Richard H; Kopans, Daniel B

    2006-07-01

    Artifacts are observed in digital breast tomosynthesis (DBT) reconstructions due to the small number of projections and the narrow angular range that are typically employed in tomosynthesis imaging. In this work, we investigate the reconstruction artifacts that are caused by high-attenuation features in breast and develop several artifact reduction methods based on a "voting strategy." The voting strategy identifies the projection(s) that would introduce artifacts to a voxel and rejects the projection(s) when reconstructing the voxel. Four approaches to the voting strategy were compared, including projection segmentation, maximum contribution deduction, one-step classification, and iterative classification. The projection segmentation method, based on segmentation of high-attenuation features from the projections, effectively reduces artifacts caused by metal and large calcifications that can be reliably detected and segmented from projections. The other three methods are based on the observation that contributions from artifact-inducing projections have higher value than those from normal projections. These methods attempt to identify the projection(s) that would cause artifacts by comparing contributions from different projections. Among the three methods, the iterative classification method provides the best artifact reduction; however, it can generate many false positive classifications that degrade the image quality. The maximum contribution deduction method and one-step classification method both reduce artifacts well from small calcifications, although the performance of artifact reduction is slightly better with the one-step classification. The combination of one-step classification and projection segmentation removes artifacts from both large and small calcifications.

  8. Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan.

    Directory of Open Access Journals (Sweden)

    Lee H Chen

    Full Text Available Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients.

  9. A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen.

    Science.gov (United States)

    Chia, Stephen K; Bramwell, Vivien H; Tu, Dongsheng; Shepherd, Lois E; Jiang, Shan; Vickery, Tammi; Mardis, Elaine; Leung, Samuel; Ung, Karen; Pritchard, Kathleen I; Parker, Joel S; Bernard, Philip S; Perou, Charles M; Ellis, Matthew J; Nielsen, Torsten O

    2012-08-15

    Gene expression profiling classifies breast cancer into intrinsic subtypes based on the biology of the underlying disease pathways. We have used material from a prospective randomized trial of tamoxifen versus placebo in premenopausal women with primary breast cancer (NCIC CTG MA.12) to evaluate the prognostic and predictive significance of intrinsic subtypes identified by both the PAM50 gene set and by immunohistochemistry. Total RNA from 398 of 672 (59%) patients was available for intrinsic subtyping with a quantitative reverse transcriptase PCR (qRT-PCR) 50-gene predictor (PAM50) for luminal A, luminal B, HER-2-enriched, and basal-like subtypes. A tissue microarray was also constructed from 492 of 672 (73%) of the study population to assess a panel of six immunohistochemical IHC antibodies to define the same intrinsic subtypes. Classification into intrinsic subtypes by the PAM50 assay was prognostic for both disease-free survival (DFS; P = 0.0003) and overall survival (OS; P = 0.0002), whereas classification by the IHC panel was not. Luminal subtype by PAM50 was predictive of tamoxifen benefit [DFS: HR, 0.52; 95% confidence interval (CI), 0.32-0.86 vs. HR, 0.80; 95% CI, 0.50-1.29 for nonluminal subtypes], although the interaction test was not significant (P = 0.24), whereas neither subtyping by central immunohistochemistry nor by local estrogen receptor (ER) or progesterone receptor (PR) status were predictive. Risk of relapse (ROR) modeling with the PAM50 assay produced a continuous risk score in both node-negative and node-positive disease. In the MA.12 study, intrinsic subtype classification by qRT-PCR with the PAM50 assay was superior to IHC profiling for both prognosis and prediction of benefit from adjuvant tamoxifen.

  10. Detection and Removal of Artifacts in Astronomical Images

    CERN Document Server

    Desai, Shantanu; Bertin, Emmanuel; Kummel, Martin; Wetzstein, Michael

    2016-01-01

    Astronomical images from optical photometric surveys are typically contaminated with transient artifacts such as cosmic rays, satellite trails and scattered light. We have developed and tested an algorithm that removes these artifacts using a deep, artifact free, static sky coadd image built up through the median combination of point spread function (PSF) homogenized, overlapping single epoch images. Transient artifacts are detected and masked in each single epoch image through comparison with an artifact free, PSF-matched simulated image that is constructed using the PSF-corrected, model fitting catalog from the artifact free coadd image together with the position variable PSF model of the single epoch image. This approach works well not only for cleaning single epoch images with worse seeing than the PSF homogenized coadd, but also the traditionally much more challenging problem of cleaning single epoch images with better seeing. In addition to masking transient artifacts, we have developed an interpolation...

  11. Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

    Science.gov (United States)

    Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

    2015-03-01

    A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

  12. Identification of co-regulated genes through Bayesian clustering of predicted regulatory binding sites.

    Science.gov (United States)

    Qin, Zhaohui S; McCue, Lee Ann; Thompson, William; Mayerhofer, Linda; Lawrence, Charles E; Liu, Jun S

    2003-04-01

    The identification of co-regulated genes and their transcription-factor binding sites (TFBS) are key steps toward understanding transcription regulation. In addition to effective laboratory assays, various computational approaches for the detection of TFBS in promoter regions of coexpressed genes have been developed. The availability of complete genome sequences combined with the likelihood that transcription factors and their cognate sites are often conserved during evolution has led to the development of phylogenetic footprinting. The modus operandi of this technique is to search for conserved motifs upstream of orthologous genes from closely related species. The method can identify hundreds of TFBS without prior knowledge of co-regulation or coexpression. Because many of these predicted sites are likely to be bound by the same transcription factor, motifs with similar patterns can be put into clusters so as to infer the sets of co-regulated genes, that is, the regulons. This strategy utilizes only genome sequence information and is complementary to and confirmative of gene expression data generated by microarray experiments. However, the limited data available to characterize individual binding patterns, the variation in motif alignment, motif width, and base conservation, and the lack of knowledge of the number and sizes of regulons make this inference problem difficult. We have developed a Gibbs sampling-based Bayesian motif clustering (BMC) algorithm to address these challenges. Tests on simulated data sets show that BMC produces many fewer errors than hierarchical and K-means clustering methods. The application of BMC to hundreds of predicted gamma-proteobacterial motifs correctly identified many experimentally reported regulons, inferred the existence of previously unreported members of these regulons, and suggested novel regulons.

  13. Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

    Directory of Open Access Journals (Sweden)

    Minseung Kim

    2015-03-01

    Full Text Available A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5% to 98.3% (±2.3% for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain achieved 10.6% (±1.0% higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

  14. In vivo validation of a computationally predicted conserved Ath5 target gene set.

    Directory of Open Access Journals (Sweden)

    Filippo Del Bene

    2007-09-01

    Full Text Available So far, the computational identification of transcription factor binding sites is hampered by the complexity of vertebrate genomes. Here we present an in silico procedure to predict target sites of a transcription factor in complex genomes using its binding site. In a first step sequence, comparison of closely related genomes identifies the binding sites in conserved cis-regulatory regions (phylogenetic footprinting. Subsequently, more remote genomes are introduced into the comparison to identify highly conserved and therefore putatively functional binding sites (phylogenetic filtering. When applied to the binding site of atonal homolog 5 (Ath5 or ATOH7, this procedure efficiently filters evolutionarily conserved binding sites out of more than 300,000 instances in a vertebrate genome. We validate a selection of the linked target genes by showing coexpression with and transcriptional regulation by Ath5. Finally, chromatin immunoprecipitation demonstrates the occupancy of the target gene promoters by Ath5. Thus, our procedure, applied to whole genomes, is a fast and predictive tool to in silico filter the target genes of a given transcription factor with defined binding site.

  15. CORECLUST: identification of the conserved CRM grammar together with prediction of gene regulation.

    Science.gov (United States)

    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.

  16. Meta4: a web application for sharing and annotating metagenomic gene predictions using web services.

    Science.gov (United States)

    Richardson, Emily J; Escalettes, Franck; Fotheringham, Ian; Wallace, Robert J; Watson, Mick

    2013-01-01

    Whole-genome shotgun metagenomics experiments produce DNA sequence data from entire ecosystems, and provide a huge amount of novel information. Gene discovery projects require up-to-date information about sequence homology and domain structure for millions of predicted proteins to be presented in a simple, easy-to-use system. There is a lack of simple, open, flexible tools that allow the rapid sharing of metagenomics datasets with collaborators in a format they can easily interrogate. We present Meta4, a flexible and extensible web application that can be used to share and annotate metagenomic gene predictions. Proteins and predicted domains are stored in a simple relational database, with a dynamic front-end which displays the results in an internet browser. Web services are used to provide up-to-date information about the proteins from homology searches against public databases. Information about Meta4 can be found on the project website, code is available on Github, a cloud image is available, and an example implementation can be seen at.

  17. Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.

    Science.gov (United States)

    Lu, Yulan; Zhou, Yuanpeng; Tian, Weidong

    2013-12-01

    Defining the target genes of distal regulatory elements (DREs), such as enhancer, repressors and insulators, is a challenging task. The recently developed Hi-C technology is designed to capture chromosome conformation structure by high-throughput sequencing, and can be potentially used to determine the target genes of DREs. However, Hi-C data are noisy, making it difficult to directly use Hi-C data to identify DRE-target gene relationships. In this study, we show that DREs-gene pairs that are confirmed by Hi-C data are strongly phylogenetic correlated, and have thus developed a method that combines Hi-C read counts with phylogenetic correlation to predict long-range DRE-target gene relationships. Analysis of predicted DRE-target gene pairs shows that genes regulated by large number of DREs tend to have essential functions, and genes regulated by the same DREs tend to be functionally related and co-expressed. In addition, we show with a couple of examples that the predicted target genes of DREs can help explain the causal roles of disease-associated single-nucleotide polymorphisms located in the DREs. As such, these predictions will be of importance not only for our understanding of the function of DREs but also for elucidating the causal roles of disease-associated noncoding single-nucleotide polymorphisms.

  18. Artifacts resulting from imaging in scattering media: a theoretical prediction.

    Science.gov (United States)

    Rohrbach, Alexander

    2009-10-01

    Scattering of illumination light from a laser is a severe problem especially when imaging in thick media. Although this effect occurs in nearly every imaging process, it can be well perceived and analyzed in configurations where the optical axes for illumination and detection are perpendicular to each other. In this paper I present a theoretical perspective of how to extend the point-spread function arithmetic from ideal imaging to realistic imaging including ghost images. These ghost images are generated by scattered light and are low-correlated with the ideal image. Numerical simulations of the propagation of four different types of illumination beams through a cluster of spheres illustrate the effects of inhomogeneous object illumination. Clear differences between a conventional plane-wave illumination, a static light-sheet, and a laterally scanned Gaussian beam, but also relative to a scanned Bessel beam, can be observed.

  19. A hemocyte gene expression signature correlated with predictive capacity of oysters to survive Vibrio infections

    Directory of Open Access Journals (Sweden)

    Rosa Rafael

    2012-06-01

    Full Text Available Abstract Background The complex balance between environmental and host factors is an important determinant of susceptibility to infection. Disturbances of this equilibrium may result in multifactorial diseases as illustrated by the summer mortality syndrome, a worldwide and complex phenomenon that affects the oysters, Crassostrea gigas. The summer mortality syndrome reveals a physiological intolerance making this oyster species susceptible to diseases. Exploration of genetic basis governing the oyster resistance or susceptibility to infections is thus a major goal for understanding field mortality events. In this context, we used high-throughput genomic approaches to identify genetic traits that may characterize inherent survival capacities in C. gigas. Results Using digital gene expression (DGE, we analyzed the transcriptomes of hemocytes (immunocompetent cells of oysters able or not able to survive infections by Vibrio species shown to be involved in summer mortalities. Hemocytes were nonlethally collected from oysters before Vibrio experimental infection, and two DGE libraries were generated from individuals that survived or did not survive. Exploration of DGE data and microfluidic qPCR analyses at individual level showed an extraordinary polymorphism in gene expressions, but also a set of hemocyte-expressed genes whose basal mRNA levels discriminate oyster capacity to survive infections by the pathogenic V. splendidus LGP32. Finally, we identified a signature of 14 genes that predicted oyster survival capacity. Their expressions are likely driven by distinct transcriptional regulation processes associated or not associated to gene copy number variation (CNV. Conclusions We provide here for the first time in oyster a gene expression survival signature that represents a useful tool for understanding mortality events and for assessing genetic traits of interest for disease resistance selection programs.

  20. Co-expressed Pathways DataBase for Tomato: a database to predict pathways relevant to a query gene.

    Science.gov (United States)

    Narise, Takafumi; Sakurai, Nozomu; Obayashi, Takeshi; Ohta, Hiroyuki; Shibata, Daisuke

    2017-06-05

    Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes. In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA. We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a

  1. Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning

    Science.gov (United States)

    Oh, Dong Hoon; Kim, Il Bin; Kim, Seok Hyeon; Ahn, Dong Hyun

    2017-01-01

    Objective The aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD. Methods We used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value <0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms. Results Hierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively. Conclusion The results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy. PMID:28138110

  2. Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions.

    Science.gov (United States)

    Shugay, Mikhail; Ortiz de Mendíbil, Iñigo; Vizmanos, José L; Novo, Francisco J

    2013-10-15

    Gene fusions resulting from chromosomal aberrations are an important cause of cancer. The complexity of genomic changes in certain cancer types has hampered the identification of gene fusions by molecular cytogenetic methods, especially in carcinomas. This is changing with the advent of next-generation sequencing, which is detecting a substantial number of new fusion transcripts in individual cancer genomes. However, this poses the challenge of identifying those fusions with greater oncogenic potential amid a background of 'passenger' fusion sequences. In the present work, we have used some recently identified genomic hallmarks of oncogenic fusion genes to develop a pipeline for the classification of fusion sequences, namely, Oncofuse. The pipeline predicts the oncogenic potential of novel fusion genes, calculating the probability that a fusion sequence behaves as 'driver' of the oncogenic process based on features present in known oncogenic fusions. Cross-validation and extensive validation tests on independent datasets suggest a robust behavior with good precision and recall rates. We believe that Oncofuse could become a useful tool to guide experimental validation studies of novel fusion sequences found during next-generation sequencing analysis of cancer transcriptomes. Oncofuse is a naive Bayes Network Classifier trained and tested using Weka machine learning package. The pipeline is executed by running a Java/Groovy script, available for download at www.unav.es/genetica/oncofuse.html.

  3. Angiotensinogen gene polymorphism predicts hypertension, and iridological constitutional classification enhances the risk for hypertension in Koreans.

    Science.gov (United States)

    Cho, Joo-Jang; Hwang, Woo-Jun; Hong, Seung-Heon; Jeong, Hyun-Ja; Lee, Hye-Jung; Kim, Hyung-Min; Um, Jae-Young

    2008-05-01

    This study investigated the relationship between iridological constitution and angiotensinogen (AGN) gene polymorphism in hypertensives. In addition to angiotensin converting enzyme gene, AGN genotype is also one of the most well studied genetic markers of hypertension. Furthermore, iridology, one of complementary and alternative medicine, is the diagnosis of the medical conditions through noting irregularities of the pigmentation in the iris. Iridological constitution has a strong familial aggregation and is implicated in heredity. Therefore, the study classified 87 hypertensive patients with familial history of cerebral infarction and controls (n = 88) according to Iris constitution, and determined AGN genotype. As a result, the AGN/TT genotype was associated with hypertension (chi2 = 13.413, p iridological constitutional classification increased the relative risk for hypertension in the subjects with AGN/T allele. These results suggest that AGN polymorphism predicts hypertension, and iridological constitutional classification enhances the risk for hypertension associated with AGN/T in a Korean population.

  4. A Digital Signal Processing Method for Gene Prediction with Improved Noise Suppression

    Directory of Open Access Journals (Sweden)

    Carreira Alex

    2004-01-01

    Full Text Available It has been observed that the protein-coding regions of DNA sequences exhibit period-three behaviour, which can be exploited to predict the location of coding regions within genes. Previously, discrete Fourier transform (DFT and digital filter-based methods have been used for the identification of coding regions. However, these methods do not significantly suppress the noncoding regions in the DNA spectrum at . Consequently, a noncoding region may inadvertently be identified as a coding region. This paper introduces a new technique (a single digital filter operation followed by a quadratic window operation that suppresses nearly all of the noncoding regions. The proposed method therefore improves the likelihood of correctly identifying coding regions in such genes.

  5. Comparative analysis of codon usage patterns and identification of predicted highly expressed genes in five Salmonella genomes

    Directory of Open Access Journals (Sweden)

    Mondal U

    2008-01-01

    Full Text Available Purpose: To anlyse codon usage patterns of five complete genomes of Salmonella , predict highly expressed genes, examine horizontally transferred pathogenicity-related genes to detect their presence in the strains, and scrutinize the nature of highly expressed genes to infer upon their lifestyle. Methods: Protein coding genes, ribosomal protein genes, and pathogenicity-related genes were analysed with Codon W and CAI (codon adaptation index Calculator. Results: Translational efficiency plays a role in codon usage variation in Salmonella genes. Low bias was noticed in most of the genes. GC3 (guanine cytosine at third position composition does not influence codon usage variation in the genes of these Salmonella strains. Among the cluster of orthologous groups (COGs, translation, ribosomal structure biogenesis [J], and energy production and conversion [C] contained the highest number of potentially highly expressed (PHX genes. Correspondence analysis reveals the conserved nature of the genes. Highly expressed genes were detected. Conclusions: Selection for translational efficiency is the major source of variation of codon usage in the genes of Salmonella . Evolution of pathogenicity-related genes as a unit suggests their ability to infect and exist as a pathogen. Presence of a lot of PHX genes in the information and storage-processing category of COGs indicated their lifestyle and revealed that they were not subjected to genome reduction.

  6. Observed and predicted changes in virulence gene frequencies at 11 loci in a local barley powdery mildew population

    DEFF Research Database (Denmark)

    Hovmøller, M.S.; Munk, L.; Østergård, H.

    1993-01-01

    a survey comprising 11 virulence loc. Predictions were based on a model where selection forces were estimated through detailed mapping in the local area of host cultivars and their resistance genes, and taking into account the changes in distribution of host cultivars during the year caused by growth......The aim of the present study was to investigate observed and predicted changes in virulence gene frequencies in a local aerial powdery mildew population subject to selection by different host cultivars in a local barley area. Observed changes were based on genotypic frequencies obtained through...... with a constant distribution of host cultivars. Significant changes in gene frequencies were observed for virulence genes subject to strong direct selection as well as for genes subject mainly to indirect selection (hitchhiking). These patterns of changes were generally as predicted from the model. The influence...

  7. Metal artifact suppression in megavoltage computed tomography

    Science.gov (United States)

    Schreiner, L. John; Rogers, Myron; Salomons, Greg; Kerr, Andrew

    2005-04-01

    There has been considerable interest in megavoltage CT (MVCT) imaging associated with the development of image guided radiation therapy. It is clear that MVCT can provide good image quality for patient setup verification with soft tissue contrast much better than noted in conventional megavoltage portal imaging. In addition, it has been observed that MVCT images exhibit considerably reduced artifacts surrounding metal implants (e.g., surgical clips, hip implants, dental fillings) compared to conventional diagnostic CT images (kVCT). When encountered, these artifacts greatly limit the usefulness of kVCT images, and a variety of solutions have been proposed to remove the artifacts, but these have met with only partial success. In this paper, we investigate the potential for CT imaging in regions surrounding metal implants using high-energy photons from a Cobalt-60 source and from a 4 MV linear accelerator. MVCT and kVCT images of contrast phantoms and a phantom containing a hip prosthesis are compared and analysed. We show that MVCT scans provide good fidelity for CT number quantification in the high-density regions of the images, and in the regions immediately adjacent to the metal implants. They also provide structural details within the high-density inserts and implants. Calculations will show that practical clinical MVCT imaging, able to detect 3% contrast objects, should be achievable with doses of about 2.5cGy. This suggests that MVCT not only has a role in radiotherapy treatment planning and guidance, but may also be indicated for surgical guidance and follow-up in regions where metal implants cannot be avoided.

  8. The use of Gene Ontology terms and KEGG pathways for analysis and prediction of oncogenes.

    Science.gov (United States)

    Xing, Zhihao; Chu, Chen; Chen, Lei; Kong, Xiangyin

    2016-11-01

    Oncogenes are a type of genes that have the potential to cause cancer. Most normal cells undergo programmed cell death, namely apoptosis, but activated oncogenes can help cells avoid apoptosis and survive. Thus, studying oncogenes is helpful for obtaining a good understanding of the formation and development of various types of cancers. In this study, we proposed a computational method, called OPM, for investigating oncogenes from the view of Gene Ontology (GO) and biological pathways. All investigated genes, including validated oncogenes retrieved from some public databases and other genes that have not been reported to be oncogenes thus far, were encoded into numeric vectors according to the enrichment theory of GO terms and KEGG pathways. Some popular feature selection methods, minimum redundancy maximum relevance and incremental feature selection, and an advanced machine learning algorithm, random forest, were adopted to analyze the numeric vectors to extract key GO terms and KEGG pathways. Along with the oncogenes, GO terms and KEGG pathways were discussed in terms of their relevance in this study. Some important GO terms and KEGG pathways were extracted using feature selection methods and were confirmed to be highly related to oncogenes. Additionally, the importance of these terms and pathways in predicting oncogenes was further demonstrated by finding new putative oncogenes based on them. This study investigated oncogenes based on GO terms and KEGG pathways. Some important GO terms and KEGG pathways were confirmed to be highly related to oncogenes. We hope that these GO terms and KEGG pathways can provide new insight for the study of oncogenes, particularly for building more effective prediction models to identify novel oncogenes. The program is available upon request. We hope that the new findings listed in this study may provide a new insight for the investigation of oncogenes. This article is part of a Special Issue entitled "System Genetics" Guest Editor

  9. Inflammation markers predict zinc transporter gene expression in women with type 2 diabetes mellitus.

    Science.gov (United States)

    Foster, Meika; Petocz, Peter; Samman, Samir

    2013-09-01

    The pathology of type 2 diabetes mellitus (DM) often is associated with underlying states of conditioned zinc deficiency and chronic inflammation. Zinc and omega-3 polyunsaturated fatty acids each exhibit anti-inflammatory effects and may be of therapeutic benefit in the disease. The present randomized, double-blind, placebo-controlled, 12-week trial was designed to investigate the effects of zinc (40 mg/day) and α-linolenic acid (ALA; 2 g/day flaxseed oil) supplementation on markers of inflammation [interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, C-reactive protein (CRP)] and zinc transporter and metallothionein gene expression in 48 postmenopausal women with type 2 DM. No significant effects of zinc or ALA supplementation were observed on inflammatory marker concentrations or fold change in zinc transporter and metallothionein gene expression. Significant increases in plasma zinc concentrations were observed over time in the groups supplemented with zinc alone or combined with ALA (P=.007 and P=.009, respectively). An impact of zinc treatment on zinc transporter gene expression was found; ZnT5 was positively correlated with Zip3 mRNA (Pzinc, while zinc supplementation abolished the relationship between ZnT5 and Zip10. IL-6 predicted the expression levels and CRP predicted the fold change of the ZnT5, ZnT7, Zip1, Zip7 and Zip10 mRNA cluster (Pzinc transporter and metallothionein gene expression support an interrelationship between zinc homeostasis and inflammation in type 2 DM.

  10. Gene expression correlation analysis predicts involvement of high- and low-confidence risk genes in different stages of prostate carcinogenesis.

    Science.gov (United States)

    Yano, Kojiro

    2010-12-01

    Whole genome association studies have identified many loci associated with the risk of prostate cancer (PC). However, very few of the genes associated with these loci have been related to specific processes of prostate carcinogenesis. Therefore I inferred biological functions associated with these risk genes using gene expression correlation analysis. PC risk genes reported in the literature were classified as having high (Plow (Phigh-confidence genes and other genes in the microarray dataset, whereas correlation between low-confidence genes and other genes in PC showed smaller decrease. Genes involved in developmental processes were significantly correlated with all risk gene categories. Ectoderm development genes, which may be related to squamous metaplasia, and genes enriched in fetal prostate stem cells (PSCs) showed strong association with the high-confidence genes. The association between the PSC genes and the low-confidence genes was weak, but genes related to neural system genes showed strong association with low-confidence genes. The high-confidence risk genes may be associated with an early stage of prostate carcinogenesis, possibly involving PSCs and squamous metaplasia. The low-confidence genes may be involved in a later stage of carcinogenesis. © 2010 Wiley-Liss, Inc.

  11. Accessing Cultural Artifacts Through Digital Companions

    DEFF Research Database (Denmark)

    Rehm, Matthias; Jensen, Martin Lynge

    2016-01-01

    This paper presents a study that explores how the introduction of a digital companion agent for a museum exploration game changes children’s engagement with the presented artworks. To this end, a mobile application was developed featuring a monster agent that has eaten the artworks, which the chi...... the children had now to find in the museum. Results show that in comparison to the paper-based version of the exploration game, children engaged in more interactions with the actual cultural artifacts and showed a significantly higher retention rate for details of the involved artworks....

  12. Hair product artifact in magnetic resonance imaging.

    Science.gov (United States)

    Chenji, Sneha; Wilman, Alan H; Mah, Dennell; Seres, Peter; Genge, Angela; Kalra, Sanjay

    2017-01-01

    The presence of metallic compounds in facial cosmetics and permanent tattoos may affect the quality of magnetic resonance imaging. We report a case study describing a signal artifact due to the use of a leave-on powdered hair dye. On reviewing the ingredients of the product, it was found to contain several metallic compounds. In lieu of this observation, we suggest that MRI centers include the use of metal- or mineral-based facial cosmetics or hair products in their screening protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Empathy, target distress, and neurohormone genes interact to predict aggression for others-even without provocation.

    Science.gov (United States)

    Buffone, Anneke E K; Poulin, Michael J

    2014-11-01

    Can empathy for others motivate aggression on their behalf? This research examined potential predictors of empathy-linked aggression including the emotional state of empathy, an empathy target's distress state, and the function of the social anxiety-modulating neuropeptides oxytocin and vasopressin. In Study 1 (N = 69), self-reported empathy combined with threat to a close other and individual differences in genes for the vasopressin receptor (AVPR1a rs3) and oxytocin receptor (OXTR rs53576) to predict self-reported aggression against a person who threatened a close other. In Study 2 (N = 162), induced empathy for a person combined with OXTR variation or with that person's distress and AVPR1a variation led to increased amount of hot sauce assigned to that person's competitor. Empathy uniquely predicts aggression and may do so by way of aspects of the human caregiving system in the form of oxytocin and vasopressin.

  14. Predicted Highly Expressed Genes in the Genomes of Streptomyces Coelicolor and Streptomyces Avermitilis and the Implications for their Metabolism.

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Gang; Culley, David E.; Zhang, Weiwen

    2005-06-01

    SUMMARY-Highly expressed genes in bacteria often have a stronger codon bias than genes expressed at lower levels. In this study, a comparative analysis of predicted highly expressed (PHX) genes in the Streptomyces coelicolor and S. avermitilis genomes was performed using the codon adaptation index (CAI) as a numerical estimator of gene expression level. Although it has been suggested that there is little heterogeneity in codon usage in G+C rich bacteria, considerable heterogeneity was found among genes in two G+C rich Streptomyces genomes. Using ribosomal protein (RP) genes as references, ~10% of the genes were predicted to be PHX genes using a CAI cutoff value of greater than 0.78 and 0.75 in S. coelicolor and S. avermitilis, respectively. Most of the PHX genes were found to be located within the conserved cores of the Streptomyces linear chromosomes. The predicted PHX genes showed good agreement with the experimental data on expression levels collected by proteomic analysis (Hesketh et al., 2002). Among all PHX genes, 368 were conserved in both genomes. These represented most of the genes essential for cell growth, including those involved in protein and DNA biosynthesis, amino acid metabolism, central intermediary and energy metabolisms. Only a few genes directly involved in biosynthesis of secondary metabolites were predicted to be PHX genes. Correspondence analysis showed that the genes responsible for biosynthesis of secondary metabolites possessed different codon usage patterns from RP genes, suggesting that they were either under strong translational selection that may have driven the codon preference in another direction, or they were acquired by horizontal transfer during their origin and evolution. Nevertheless, several key genes responsible for producing precursors for secondary metabolites, such as crotonyl-CoA reductase and propionyl-CoA carboxylase, and genes necessary for initiation of secondary metabolism, such as adenosylmethionine synthetase were

  15. Bayesian state space models for inferring and predicting temporal gene expression profiles.

    Science.gov (United States)

    Liang, Yulan; Kelemen, Arpad

    2007-12-01

    Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models. (c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

  16. RFMirTarget: predicting human microRNA target genes with a random forest classifier.

    Directory of Open Access Journals (Sweden)

    Mariana R Mendoza

    Full Text Available MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.

  17. Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: A Children's Oncology Group study

    NARCIS (Netherlands)

    H. Kang; C.S. Wilson (Carla); R. Harvey (R.); I.-M. Chen (I.-Ming); M.H. Murphy (Maurice); S.R. Atlas (Susan); E.J. Bedrick (Edward); M. Devidas (Meenakshi); A.J. Carroll; B.W. Robinson (Blaine); R.W. Stam (Ronald); M.G. Valsecchi (Maria Grazia); R. Pieters (Rob); N.A. Heerema (Nyla); J.M. Hilden (Joanne); C.A. Felix (Carolyn); G.H. Reaman (Gregory); B. Camitta (Bruce); N.J. Winick (Naomi); W.L. Carroll (William); S.D. Dreyer; S.P. Hunger (Stephen); S.F. Willman (Sami )

    2012-01-01

    textabstractGene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expressio

  18. Detection of artifacts from high energy bursts in neonatal EEG.

    Science.gov (United States)

    Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

    2013-11-01

    Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the

  19. Immunohistochemical NF1 analysis does not predict NF1 gene mutation status in pheochromocytoma.

    Science.gov (United States)

    Stenman, Adam; Svahn, Fredrika; Welander, Jenny; Gustavson, Boel; Söderkvist, Peter; Gimm, Oliver; Juhlin, C Christofer

    2015-03-01

    Pheochromocytomas (PCCs) are tumors originating from the adrenal medulla displaying a diverse genetic background. While most PCCs are sporadic, about 40 % of the tumors have been associated with constitutional mutations in one of at least 14 known susceptibility genes. As 25 % of sporadic PCCs harbor somatic neurofibromin 1 gene (NF1) mutations, NF1 has been established as the most recurrently mutated gene in PCCs. To be able to pinpoint NF1-related pheochromocytoma (PCC) disease in clinical practice could facilitate the detection of familial cases, but the large size of the NF1 gene makes standard DNA sequencing methods cumbersome. The aim of this study was to examine whether mutations in the NF1 gene could be predicted by immunohistochemistry as a method to identify cases for further genetic characterization. Sixty-seven PCCs obtained from 67 unselected patients for which the somatic and constitutional mutational status of NF1 was known (49 NF1 wild type, 18 NF1 mutated) were investigated for NF1 protein immunoreactivity, and the results were correlated to clinical and genetic data. NF1 immunoreactivity was absent in the majority of the PCCs (44/67; 66 %), including 13 out of 18 cases (72 %) with a somatic or constitutional NF1 mutation. However, only a minority of the NF1 wild-type PCCs (18/49; 37 %) displayed retained NF1 immunoreactivity, thereby diminishing the specificity of the method. We conclude that NF1 immunohistochemistry alone is not a sufficient method to distinguish between NF1-mutated and non-mutated PCCs. In the clinical context, genetic screening therefore remains the most reliable tool to detect NF1-mutated PCCs.

  20. Recognizing scientific artifacts in biomedical literature.

    Science.gov (United States)

    Groza, Tudor; Hassanzadeh, Hamed; Hunter, Jane

    2013-01-01

    Today's search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications. The performance achieved by the classifiers ranges from 15.30% to 78.39%, subject to the target class. The set of features used for classification has led to promising results. Furthermore, their use strictly in a local, publication scope, ie, without aggregating corpus-wide statistics, increases the versatility of the ensemble of classifiers and enables its direct applicability without the necessity of re-training.

  1. Ontological System for Context Artifacts and Resources

    Science.gov (United States)

    Huang, T.; Chung, N. T.; Mukherjee, R. M.

    2012-12-01

    The Adaptive Vehicle Make (AVM) program is a portfolio of programs, managed by the Defense Advanced Research Projects Agency (DARPA). It was established to revolutionize how DoD designs, verifies, and manufactures complex defense systems and vehicles. The Component, Context, and Manufacturing Model Library (C2M2L; pronounced "camel") seeks to develop domain-specific models needed to enable design, verification, and fabrication of the Fast Adaptable Next-Generation (FANG) infantry fighting vehicle using in its overall infrastructure. Terrain models are being developed to represent the surface/fluid that an amphibious infantry fighting vehicle would traverse, ranging from paved road surfaces to rocky, mountainous terrain, slope, discrete obstacles, mud, sand snow, and water fording. Context models are being developed to provide additional data for environmental factors, such as: humidity, wind speed, particulate presence and character, solar radiation, cloud cover, precipitation, and more. The Ontological System for Context Artifacts and Resources (OSCAR) designed and developed at the Jet Propulsion Laboratory is semantic web data system that enables context artifacts to be registered and searched according to their meaning, rather than indexed according to their syntactic structure alone (as in the case for traditional search engines). The system leverages heavily on the Semantic Web for Earth and Environmental Terminology (SWEET) ontologies to model physical terrain environment and context model characteristics. In this talk, we focus on the application of the SWEET ontologies and the design of the OSCAR system architecture.

  2. Chronic and Acute Stress, Gender, and Serotonin Transporter Gene-Environment Interactions Predicting Depression Symptoms in Youth

    Science.gov (United States)

    Hammen, Constance; Brennan, Patricia A.; Keenan-Miller, Danielle; Hazel, Nicholas A.; Najman, Jake M.

    2010-01-01

    Background: Many recent studies of serotonin transporter gene by environment effects predicting depression have used stress assessments with undefined or poor psychometric methods, possibly contributing to wide variation in findings. The present study attempted to distinguish between effects of acute and chronic stress to predict depressive…

  3. Prediction of Associations between microRNAs and Gene Expression in Glioma Biology.

    Directory of Open Access Journals (Sweden)

    Stefan Wuchty

    Full Text Available Despite progress in the determination of miR interactions, their regulatory role in cancer is only beginning to be unraveled. Utilizing gene expression data from 27 glioblastoma samples we found that the mere knowledge of physical interactions between specific mRNAs and miRs can be used to determine associated regulatory interactions, allowing us to identify 626 associated interactions, involving 128 miRs that putatively modulate the expression of 246 mRNAs. Experimentally determining the expression of miRs, we found an over-representation of over(under-expressed miRs with various predicted mRNA target sequences. Such significantly associated miRs that putatively bind over-expressed genes strongly tend to have binding sites nearby the 3'UTR of the corresponding mRNAs, suggesting that the presence of the miRs near the translation stop site may be a factor in their regulatory ability. Our analysis predicted a significant association between miR-128 and the protein kinase WEE1, which we subsequently validated experimentally by showing that the over-expression of the naturally under-expressed miR-128 in glioma cells resulted in the inhibition of WEE1 in glioblastoma cells.

  4. Computational Prediction of MicroRNAs from Toxoplasma gondii Potentially Regulating the Hosts’ Gene Expression

    Institute of Scientific and Technical Information of China (English)

    Muserref Duygu Sacar; Caner Bagc; Jens Allmer

    2014-01-01

    MicroRNAs (miRNAs) were discovered two decades ago, yet there is still a great need for further studies elucidating their genesis and targeting in different phyla. Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning. Toxoplasma gondii, a parasite residing within the cells of its hosts like human, uses miRNAs for its post-transcriptional gene reg-ulation. It may also regulate its hosts’ gene expression, which has been shown in brain cancer. Since previous studies have shown that overexpressed miRNAs within the host are causal for disease onset, we hypothesized that T. gondii could export miRNAs into its host cell. We computationally predicted all hairpins from the genome of T. gondii and used mouse and human models to filter possible candidates. These were then further compared to known miRNAs in human and rodents and their expression was examined for T. gondii grown in mouse and human hosts, respectively. We found that among the millions of potential hairpins in T. gondii, only a few thousand pass filtering using a human or mouse model and that even fewer of those are expressed. Since they are expressed and differentially expressed in rodents and human, we suggest that there is a chance that T. gondii may export miRNAs into its hosts for direct regulation.

  5. Computational prediction of microRNAs from Toxoplasma gondii potentially regulating the hosts' gene expression.

    Science.gov (United States)

    Saçar, Müşerref Duygu; Bağcı, Caner; Allmer, Jens

    2014-10-01

    MicroRNAs (miRNAs) were discovered two decades ago, yet there is still a great need for further studies elucidating their genesis and targeting in different phyla. Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning. Toxoplasma gondii, a parasite residing within the cells of its hosts like human, uses miRNAs for its post-transcriptional gene regulation. It may also regulate its hosts' gene expression, which has been shown in brain cancer. Since previous studies have shown that overexpressed miRNAs within the host are causal for disease onset, we hypothesized that T. gondii could export miRNAs into its host cell. We computationally predicted all hairpins from the genome of T. gondii and used mouse and human models to filter possible candidates. These were then further compared to known miRNAs in human and rodents and their expression was examined for T. gondii grown in mouse and human hosts, respectively. We found that among the millions of potential hairpins in T. gondii, only a few thousand pass filtering using a human or mouse model and that even fewer of those are expressed. Since they are expressed and differentially expressed in rodents and human, we suggest that there is a chance that T. gondii may export miRNAs into its hosts for direct regulation.

  6. Prediction of optimal gene functions for osteosarcoma using network-based- guilt by association method based on gene oncology and microarray profile.

    Science.gov (United States)

    Chen, Xinrang

    2017-06-01

    In the current study, we planned to predict the optimal gene functions for osteosarcoma (OS) by integrating network-based method with guilt by association (GBA) principle (called as network-based gene function inference approach) based on gene oncology (GO) data and gene expression profile. To begin with, differentially expressed genes (DEGs) were extracted using linear models for microarray data (LIMMA) package. Then, construction of differential co-expression network (DCN) relying on DEGs was implemented, and sub-DCN was identified using Spearman correlation coefficient (SCC). Subsequently, GO annotations for OS were collected according to known confirmed database and DEGs. Ultimately, gene functions were predicted by means of GBA principle based on the area under the curve (AUC) for GO terms, and we determined GO terms with AUC >0.7 as the optimal gene functions for OS. Totally, 123 DEGs and 137 GO terms were obtained for further analysis. A DCN was constructed, which included 123 DEGs and 7503 interactions. A total of 105 GO terms were identified when the threshold was set as AUC >0.5, which had a good classification performance. Among these 105 GO terms, 2 functions had the AUC >0.7 and were determined as the optimal gene functions including angiogenesis (AUC =0.767) and regulation of immune system process (AUC =0.710). These gene functions appear to have potential for early detection and clinical treatment of OS in the future.

  7. Can survival prediction be improved by merging gene expression data sets?

    Directory of Open Access Journals (Sweden)

    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

  8. Contrast Reversal of Topography Artifacts in a Transmission SNOM

    Institute of Scientific and Technical Information of China (English)

    LI Zhi; WANG Shu-Feng; ZHANG Jia-Sen; GONG Qi-Huang

    2005-01-01

    @@ We demonstrate the contrast reversal behaviour of topography artifacts by changing the diameter of the collection diaphragm in a transmission scanning near-field optical microscopy (SNOM). This originates from the change of the approach curves. Such contrast reversal phenomenon is used to distinguish the artifact signal from the true optical signal of the SNOM image. We also show that continuously changing the diaphragm to a proper diameter can greatly reduce topography artifacts.

  9. Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.

    Directory of Open Access Journals (Sweden)

    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.

  10. A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer.

    Science.gov (United States)

    Chanrion, Maïa; Negre, Vincent; Fontaine, Hélène; Salvetat, Nicolas; Bibeau, Frédéric; Mac Grogan, Gaëtan; Mauriac, Louis; Katsaros, Dionyssios; Molina, Franck; Theillet, Charles; Darbon, Jean-Marie

    2008-03-15

    The identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor-positive cancers. A series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature. We defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the "Adjuvant!" software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria. This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.

  11. The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction

    Directory of Open Access Journals (Sweden)

    Garzón-Martínez Gina A

    2012-04-01

    Full Text Available Abstract Background Physalis peruviana commonly known as Cape gooseberry is a member of the Solanaceae family that has an increasing popularity due to its nutritional and medicinal values. A broad range of genomic tools is available for other Solanaceae, including tomato and potato. However, limited genomic resources are currently available for Cape gooseberry. Results We report the generation of a total of 652,614 P. peruviana Expressed Sequence Tags (ESTs, using 454 GS FLX Titanium technology. ESTs, with an average length of 371 bp, were obtained from a normalized leaf cDNA library prepared using a Colombian commercial variety. De novo assembling was performed to generate a collection of 24,014 isotigs and 110,921 singletons, with an average length of 1,638 bp and 354 bp, respectively. Functional annotation was performed using NCBI’s BLAST tools and Blast2GO, which identified putative functions for 21,191 assembled sequences, including gene families involved in all the major biological processes and molecular functions as well as defense response and amino acid metabolism pathways. Gene model predictions in P. peruviana were obtained by using the genomes of Solanum lycopersicum (tomato and Solanum tuberosum (potato. We predict 9,436 P. peruviana sequences with multiple-exon models and conserved intron positions with respect to the potato and tomato genomes. Additionally, to study species diversity we developed 5,971 SSR markers from assembled ESTs. Conclusions We present the first comprehensive analysis of the Physalis peruviana leaf transcriptome, which will provide valuable resources for development of genetic tools in the species. Assembled transcripts with gene models could serve as potential candidates for marker discovery with a variety of applications including: functional diversity, conservation and improvement to increase productivity and fruit quality. P. peruviana was estimated to be phylogenetically branched out before the

  12. Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles.

    Directory of Open Access Journals (Sweden)

    Weijia Zhang

    Full Text Available microRNAs (miRNAs play crucial roles in post-transcriptional gene regulation of both plants and mammals, and dysfunctions of miRNAs are often associated with tumorigenesis and development through the effects on their target messenger RNAs (mRNAs. Identifying miRNA functions is critical for understanding cancer mechanisms and determining the efficacy of drugs. Computational methods analyzing high-throughput data offer great assistance in understanding the diverse and complex relationships between miRNAs and mRNAs. However, most of the existing methods do not fully utilise the available knowledge in biology to reduce the uncertainty in the modeling process. Therefore it is desirable to develop a method that can seamlessly integrate existing biological knowledge and high-throughput data into the process of discovering miRNA regulation mechanisms.In this article we present an integrative framework, CIDER (Causal miRNA target Discovery with Expression profile and Regulatory knowledge, to predict miRNA targets. CIDER is able to utilise a variety of gene regulation knowledge, including transcriptional and post-transcriptional knowledge, and to exploit gene expression data for the discovery of miRNA-mRNA regulatory relationships. The benefits of our framework is demonstrated by both simulation study and the analysis of the epithelial-to-mesenchymal transition (EMT and the breast cancer (BRCA datasets. Our results reveal that even a limited amount of either Transcription Factor (TF-miRNA or miRNA-mRNA regulatory knowledge improves the performance of miRNA target prediction, and the combination of the two types of knowledge enhances the improvement further. Another useful property of the framework is that its performance increases monotonically with the increase of regulatory knowledge.

  13. Bioinformatic Prediction of Gene Functions Regulated by Quorum Sensing in the Bioleaching Bacterium Acidithiobacillus ferrooxidans

    Directory of Open Access Journals (Sweden)

    Alvaro Banderas

    2013-08-01

    Full Text Available The biomining bacterium Acidithiobacillus ferrooxidans oxidizes sulfide ores and promotes metal solubilization. The efficiency of this process depends on the attachment of cells to surfaces, a process regulated by quorum sensing (QS cell-to-cell signalling in many Gram-negative bacteria. At. ferrooxidans has a functional QS system and the presence of AHLs enhances its attachment to pyrite. However, direct targets of the QS transcription factor AfeR remain unknown. In this study, a bioinformatic approach was used to infer possible AfeR direct targets based on the particular palindromic features of the AfeR binding site. A set of Hidden Markov Models designed to maintain palindromic regions and vary non-palindromic regions was used to screen for putative binding sites. By annotating the context of each predicted binding site (PBS, we classified them according to their positional coherence relative to other putative genomic structures such as start codons, RNA polymerase promoter elements and intergenic regions. We further used the Multiple EM for Motif Elicitation algorithm (MEME to further filter out low homology PBSs. In summary, 75 target-genes were identified, 34 of which have a higher confidence level. Among the identified genes, we found afeR itself, zwf, genes encoding glycosyltransferase activities, metallo-beta lactamases, and active transport-related proteins. Glycosyltransferases and Zwf (Glucose 6-phosphate-1-dehydrogenase might be directly involved in polysaccharide biosynthesis and attachment to minerals by At. ferrooxidans cells during the bioleaching process.

  14. Melanopsin Gene Variations Interact With Season to Predict Sleep Onset and Chronotype

    Science.gov (United States)

    Roecklein, Kathryn A.; Wong, Patricia M.; Franzen, Peter L.; Hasler, Brant P.; Wood-Vasey, W. Michael; Nimgaonkar, Vishwajit L.; Miller, Megan A.; Kepreos, Kyle M.; Ferrell, Robert E.; Manuck, Stephen B.

    2013-01-01

    The human melanopsin gene has been reported to mediate risk for seasonal affective disorder (SAD), which is hypothesized to be caused by decreased photic input during winter when light levels fall below threshold, resulting in differences in circadian phase and/or sleep. However, it is unclear if melanopsin increases risk of SAD by causing differences in sleep or circadian phase, or if those differences are symptoms of the mood disorder. To determine if melanopsin sequence variations are associated with differences in sleep-wake behavior among those not suffering from a mood disorder, the authors tested associations between melanopsin gene polymorphisms and self-reported sleep timing (sleep onset and wake time) in a community sample (N = 234) of non-Hispanic Caucasian participants (age 30–54 yrs) with no history of psychological, neurological, or sleep disorders. The authors also tested the effect of melanopsin variations on differences in preferred sleep and activity timing (i.e., chronotype), which may reflect differences in circadian phase, sleep homeostasis, or both. Daylength on the day of assessment was measured and included in analyses. DNA samples were genotyped for melanopsin gene polymorphisms using fluorescence polarization. P10L genotype interacted with daylength to predict self-reported sleep onset (interaction p seasonal patterns of recurrence or exacerbation. PMID:22881342

  15. Gastric microbiota and predicted gene functions are altered after subtotal gastrectomy in patients with gastric cancer.

    Science.gov (United States)

    Tseng, Ching-Hung; Lin, Jaw-Town; Ho, Hsiu J; Lai, Zi-Lun; Wang, Chang-Bi; Tang, Sen-Lin; Wu, Chun-Ying

    2016-02-10

    Subtotal gastrectomy (i.e., partial removal of the stomach), a surgical treatment for early-stage distal gastric cancer, is usually accompanied by highly selective vagotomy and Billroth II reconstruction, leading to dramatic changes in the gastric environment. Based on accumulating evidence of a strong link between human gut microbiota and host health, a 2-year follow-up study was conducted to characterize the effects of subtotal gastrectomy. Gastric microbiota and predicted gene functions inferred from 16S rRNA gene sequencing were analyzed before and after surgery. The results demonstrated that gastric microbiota is significantly more diverse after surgery. Ralstonia and Helicobacter were the top two genera of discriminant abundance in the cancerous stomach before surgery, while Streptococcus and Prevotella were the two most abundant genera after tumor excision. Furthermore, N-nitrosation genes were prevalent before surgery, whereas bile salt hydrolase, NO and N2O reductase were prevalent afterward. To our knowledge, this is the first report to document changes in gastric microbiota before and after surgical treatment of stomach cancer.

  16. Contrast artifacts in tapping tip atomic force microscopy

    DEFF Research Database (Denmark)

    Kyhle, Anders; Sørensen, Alexis Hammer; Zandbergen, Julie Bjerring;

    1998-01-01

    When recording images with an atomic force microscope using the resonant vibrating cantilever mode, surprising strange results are often achieved. Typical artifacts are strange contours, unexpected height shifts, and sudden changes of the apparent resolution in the acquired images. Such artifacts...... interaction. The oscillating cantilever will be in a specific swing mode according to which type of interaction is dominating, and it is the switching between these modes that is responsible for a range of artifacts observed during image acquisition. This includes the artifact often referred to as "contrast...

  17. Automatic identification of artifacts in electrodermal activity data.

    Science.gov (United States)

    Taylor, Sara; Jaques, Natasha; Chen, Weixuan; Fedor, Szymon; Sano, Akane; Picard, Rosalind

    2015-01-01

    Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

  18. Picking Up Artifacts: Storyboarding as a Gateway to Reuse

    Science.gov (United States)

    Wahid, Shahtab; Branham, Stacy M.; Cairco, Lauren; McCrickard, D. Scott; Harrison, Steve

    Storyboarding offers designers the opportunity to illustrate a visual narrative of use. Because designers often refer to past ideas, we argue storyboards can be constructed by reusing shared artifacts. We present a study in which we explore how designers reuse artifacts consisting of images and rationale during storyboard construction. We find images can aid in accessing rationale and that connections among features aid in deciding what to reuse, creating new artifacts, and constructing. Based on requirements derived from our findings, we present a storyboarding tool, PIC-UP, to facilitate artifact sharing and reuse and evaluate its use in an exploratory study. We conclude with remarks on facilitating reuse and future work.

  19. Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

    Science.gov (United States)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

    2016-11-16

    Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

  20. Artifact versus arrhythmia in pseudo-polymorphic tachycardia; case report

    Directory of Open Access Journals (Sweden)

    Ahmed V

    2015-04-01

    Full Text Available Vaseem Ahmed, Anish Patel, Abhishek Sharma, Dennis Bloomfield Department of Medicine, Richmond University Medical Center, Staten Island, NY, USA Abstract: We present the case of a young male patient in sinus rhythm whose electrocardiogram (ECG was initially misinterpreted as ventricular tachycardia. Electrocardiographic artifact appearing to be ventricular tachycardia commonly occurs and ECG criteria have been described to aid in the discrimination between artifact and true arrhythmia. There are many causes of artifacts and prompt recognition is important to prevent unnecessary interventions. Keywords: artifact, ventricular tachycardia, pseudo-ventricular tachycardia, notch sign, sinus sign

  1. CT metal artifact reduction by soft inequality constraints

    Science.gov (United States)

    Chukalina, Marina; Nikolaev, Dmitry; Sokolov, Valerii; Ingacheva, Anastasiya; Buzmakov, Alexey; Prun, Victor

    2015-12-01

    The artifacts (known as metal-like artifacts) arising from incorrect reconstruction may obscure or simulate pathology in medical applications, hide or mimic cracks and cavities in the scanned objects in industrial tomographic scans. One of the main reasons caused such artifacts is photon starvation on the rays which go through highly absorbing regions. We indroduce a way to suppress such artifacts in the reconstructions using soft penalty mimicing linear inequalities on the photon starved rays. An efficient algorithm to use such information is provided and the effect of those inequalities on the reconstruction quality is studied.

  2. Response-predictive gene expression profiling of glioma progenitor cells in vitro.

    Directory of Open Access Journals (Sweden)

    Sylvia Moeckel

    Full Text Available BACKGROUND: High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist. METHODS: In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a to enrich specimens for brain tumor initiating cells and (b to confront cells with a therapeutic agent before expression profiling. RESULTS: As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro. CONCLUSION: For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.

  3. Gene expression signatures that predict radiation exposure in mice and humans.

    Directory of Open Access Journals (Sweden)

    Holly K Dressman

    2007-04-01

    Full Text Available BACKGROUND: The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation. METHODS AND FINDINGS: We have made use of gene expression analysis of peripheral blood (PB mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans. CONCLUSIONS: We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

  4. Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data

    CERN Document Server

    Kastrin, Andrej

    2010-01-01

    Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this prob- lem is by using dimension reduction statistical techniques. Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data. In this paper, we study the potential of RM-based modeling in dimensionality reduction with binarized microarray gene expression data and investigate its prediction ac- curacy in the context of class prediction using linear discriminant analysis. Two different publicly available microarray data sets are used to illustrate a general framework of the approach. Performance of the proposed method is assessed by re-randomization s...

  5. Prediction of the prognosis of breast cancer in routine histologic specimens using a simplified, low-cost gene expression signature

    DEFF Research Database (Denmark)

    Marcell, S.A.; Balazs, A.; Emese, A.;

    2013-01-01

    Prediction of the prognosis of breast cancer in routine histologic specimens using a simplified, low-cost gene expression signature Background: Grade 2 breast carcinomas do not form a uniform prognostic group. Aim: To extend the number of patients and the investigated genes of a previously...... identified prognostic signature described by the authors that reflect chromosomal instability in order to refine characterization of grade 2 breast cancers and identify driver genes. Methods: Using publicly available databases, the authors selected 9 target and 3 housekeeping genes that are capable to divide...... prognosis groups. Centroid-based ranking showed that 3 genes, FOXM1, TOP2A and CLDN4 were able to separate the good and poor prognostic groups of grade 2 breast carcinomas. Conclusion: Using appropriately selected control genes, a limited set of genes is able to split prognostic groups of breast carcinomas...

  6. Mining predicted essential genes of Brugia malayi for nematode drug targets.

    Directory of Open Access Journals (Sweden)

    Sanjay Kumar

    Full Text Available We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  7. Polymorphism at the Clock gene predicts phenology of long-distance migration in birds.

    Science.gov (United States)

    Saino, Nicola; Bazzi, Gaia; Gatti, Emanuele; Caprioli, Manuela; Cecere, Jacopo G; Possenti, Cristina D; Galimberti, Andrea; Orioli, Valerio; Bani, Luciano; Rubolini, Diego; Gianfranceschi, Luca; Spina, Fernando

    2015-04-01

    Dissecting phenotypic variance in life history traits into its genetic and environmental components is at the focus of evolutionary studies and of pivotal importance to identify the mechanisms and predict the consequences of human-driven environmental change. The timing of recurrent life history events (phenology) is under strong selection, but the study of the genes that control potential environmental canalization in phenological traits is at its infancy. Candidate genes for circadian behaviour entrained by photoperiod have been screened as potential controllers of phenological variation of breeding and moult in birds, with inconsistent results. Despite photoperiodic control of migration is well established, no study has reported on migration phenology in relation to polymorphism at candidate genes in birds. We analysed variation in spring migration dates within four trans-Saharan migratory species (Luscinia megarhynchos; Ficedula hypoleuca; Anthus trivialis; Saxicola rubetra) at a Mediterranean island in relation to Clock and Adcyap1 polymorphism. Individuals with larger number of glutamine residues in the poly-Q region of Clock gene migrated significantly later in one or, respectively, two species depending on sex and whether the within-individual mean length or the length of the longer Clock allele was considered. The results hinted at dominance of the longer Clock allele. No significant evidence for migration date to covary with Adcyap1 polymorphism emerged. This is the first evidence that migration phenology is associated with Clock in birds. This finding is important for evolutionary studies of migration and sheds light on the mechanisms that drive bird phenological changes and population trends in response to climate change.

  8. Tissue-based microarray expression of genes predictive of metastasis in uveal melanoma and differentially expressed in metastatic uveal melanoma.

    Science.gov (United States)

    Demirci, Hakan; Reed, David; Elner, Victor M

    2013-10-01

    To screen the microarray expression of CDH1, ECM1, EIF1B, FXR1, HTR2B, ID2, LMCD1, LTA4H, MTUS1, RAB31, ROBO1, and SATB1 genes which are predictive of primary uveal melanoma metastasis, and NFKB2, PTPN18, MTSS1, GADD45B, SNCG, HHIP, IL12B, CDK4, RPLP0, RPS17, RPS12 genes that are differentially expressed in metastatic uveal melanoma in normal whole human blood and tissues prone to metastatic involvement by uveal melanoma. We screened the GeneNote and GNF BioGPS databases for microarray analysis of genes predictive of primary uveal melanoma metastasis and those differentially expressed in metastatic uveal melanoma in normal whole blood, liver, lung and skin. Microarray analysis showed expression of all 22 genes in normal whole blood, liver, lung and skin, which are the most common sites of metastases. In the GNF BioGPS database, data for expression of the HHIP gene in normal whole blood and skin was not complete. Microarray analysis of genes predicting systemic metastasis of uveal melanoma and genes differentially expressed in metastatic uveal melanoma may not be used as a biomarker for metastasis in whole blood, liver, lung, and skin. Their expression in tissues prone to metastasis may suggest that they play a role in tropism of uveal melanoma metastasis to these tissues.

  9. Tissue-Based Microarray Expression of Genes Predictive of Metastasis in Uveal Melanoma and Differentially Expressed in Metastatic Uveal Melanoma

    Directory of Open Access Journals (Sweden)

    Hakan Demirci

    2013-01-01

    Full Text Available Purpose: To screen the microarray expression of CDH1, ECM1, EIF1B, FXR1, HTR2B, ID2, LMCD1, LTA4H, MTUS1, RAB31, ROBO1, and SATB1 genes which are predictive of primary uveal melanoma metastasis, and NFKB2, PTPN18, MTSS1, GADD45B, SNCG, HHIP, IL12B, CDK4, RPLP0, RPS17, RPS12 genes that are differentially expressed in metastatic uveal melanoma in normal whole human blood and tissues prone to metastatic involvement by uveal melanoma. Methods: We screened the GeneNote and GNF BioGPS databases for microarray analysis of genes predictive of primary uveal melanoma metastasis and those differentially expressed in metastatic uveal melanoma in normal whole blood, liver, lung and skin. Results: Microarray analysis showed expression of all 22 genes in normal whole blood, liver, lung and skin, which are the most common sites of metastases. In the GNF BioGPS database, data for expression of the HHIP gene in normal whole blood and skin was not complete. Conclusions: Microarray analysis of genes predicting systemic metastasis of uveal melanoma and genes differentially expressed in metastatic uveal melanoma may not be used as a biomarker for metastasis in whole blood, liver, lung, and skin. Their expression in tissues prone to metastasis may suggest that they play a role in tropism of uveal melanoma metastasis to these tissues.

  10. Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

    Science.gov (United States)

    Sivaraks, Haemwaan; Ratanamahatana, Chotirat Ann

    2015-01-01

    Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

  11. Prediction of effective RNA interference targets and pathway-related genes in lepidopteran insects by RNA sequencing analysis.

    Science.gov (United States)

    Guan, Ruo-Bing; Li, Hai-Chao; Miao, Xue-Xia

    2017-01-06

    When using RNAi to study gene functions in Lepidoptera insects, we discovered that some genes could not be suppressed, instead, their expression levels could be up-regulated by dsRNA. To predict which genes could be easily silenced, we treated the Asian corn borer (Ostrinia furnacalis) with dsGFP and dsMLP. A transcriptome sequence analysis was conducted using the cDNAs 6 h after treatment with dsRNA. The results indicated that 160 genes were up-regulated and 44 genes were down-regulated by the two dsRNAs. Then, 50 co-up-regulated, 25 co-down-regulated and 43 unaffected genes were selected to determine their RNAi responses. All the 25 down-regulated genes were knocked down by their corresponding dsRNA. However, several of the up-regulated and unaffected genes were up-regulated when treated with their corresponding dsRNAs instead of being knocked-down. The genes up-regulated by the dsGFP treatment may be involved in insect immune responses or the RNAi pathway. When the immune-related genes were excluded, only seven genes were induced by dsGFP, including ago-2 and dicer-2. These results not only provide a reference for efficient RNAi targets predication, but also provide some potential RNAi pathway-related genes for further study. This article is protected by copyright. All rights reserved.

  12. Medical image of the week: polysomnogram artifact

    Directory of Open Access Journals (Sweden)

    Bartell J

    2015-02-01

    Full Text Available A 54 year-old man with a past medical history of attention deficit hyperactivity disorder (ADHD, low back pain, and paroxysmal supraventricular tachycardia presented to the sleep laboratory for evaluation of sleep disordered breathing. Pertinent medications include fluoxetine, ambien, and clonazepam. His Epworth sleepiness score was 18. He had a total sleep time of 12 min. On the night of his sleep study, the patient was restless and repeatedly changed positions in bed. Figures 1 and 2 show the artifact determined to be lead displacement of O1M2 after the patient shifted in bed, inadvertently removing one of his scalp electrodes. The sine waves are 60 Hz in frequency. Once the problem was identified, the lead was quickly replaced to its proper position.

  13. Defining the cutoff value of MGMT gene promoter methylation and its predictive capacity in glioblastoma.

    Science.gov (United States)

    Brigliadori, Giovanni; Foca, Flavia; Dall'Agata, Monia; Rengucci, Claudia; Melegari, Elisabetta; Cerasoli, Serenella; Amadori, Dino; Calistri, Daniele; Faedi, Marina

    2016-06-01

    Despite advances in the treatment of glioblastoma (GBM), median survival is 12-15 months. O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation status is acknowledged as a predictive marker for temozolomide (TMZ) treatment. When MGMT promoter values fall into a "methylated" range, a better response to chemotherapy is expected. However, a cutoff that discriminates between "methylated" and "unmethylated" status has yet to be defined. We aimed to identify the best cutoff value and to find out whether variability in methylation profiles influences the predictive capacity of MGMT promoter methylation. Data from 105 GBM patients treated between 2008 and 2013 were analyzed. MGMT promoter methylation status was determined by analyzing 10 CpG islands by pyrosequencing. Patients were treated with radiotherapy followed by TMZ. MGMT promoter methylation status was classified into unmethylated 0-9 %, methylated 10-29 % and methylated 30-100 %. Statistical analysis showed that an assumed methylation cutoff of 9 % led to an overestimation of responders. All patients in the 10-29 % methylation group relapsed before the 18-month evaluation. Patients with a methylation status ≥30 % showed a median overall survival of 25.2 months compared to 15.2 months in all other patients, confirming this value as the best methylation cutoff. Despite wide variability among individual profiles, single CpG island analysis did not reveal any correlation between single CpG island methylation values and relapse or death. Specific CpG island methylation status did not influence the predictive value of MGMT. The predictive role of MGMT promoter methylation was maintained only with a cutoff value ≥30 %.

  14. An extension to artifact-free projection overlaps

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Jianyu, E-mail: jianyulin@hotmail.com [Department of Electrical and Computer Engineering, Curtin University, GPO Box U1987, Perth, Western Australia 6845 (Australia)

    2015-05-15

    Purpose: In multipinhole single photon emission computed tomography, the overlapping of projections has been used to increase sensitivity. Avoiding artifacts in the reconstructed image associated with projection overlaps (multiplexing) is a critical issue. In our previous report, two types of artifact-free projection overlaps, i.e., projection overlaps that do not lead to artifacts in the reconstructed image, were formally defined and proved, and were validated via simulations. In this work, a new proposition is introduced to extend the previously defined type-II artifact-free projection overlaps so that a broader range of artifact-free overlaps is accommodated. One practical purpose of the new extension is to design a baffle window multipinhole system with artifact-free projection overlaps. Methods: First, the extended type-II artifact-free overlap was theoretically defined and proved. The new proposition accommodates the situation where the extended type-II artifact-free projection overlaps can be produced with incorrectly reconstructed portions in the reconstructed image. Next, to validate the theory, the extended-type-II artifact-free overlaps were employed in designing the multiplexing multipinhole spiral orbit imaging systems with a baffle window. Numerical validations were performed via simulations, where the corresponding 1-pinhole nonmultiplexing reconstruction results were used as the benchmark for artifact-free reconstructions. The mean square error (MSE) was the metric used for comparisons of noise-free reconstructed images. Noisy reconstructions were also performed as part of the validations. Results: Simulation results show that for noise-free reconstructions, the MSEs of the reconstructed images of the artifact-free multiplexing systems are very similar to those of the corresponding 1-pinhole systems. No artifacts were observed in the reconstructed images. Therefore, the testing results for artifact-free multiplexing systems designed using the

  15. Prior-based artifact correction (PBAC) in computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Heußer, Thorsten, E-mail: thorsten.heusser@dkfz-heidelberg.de; Brehm, Marcus [Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg (Germany); Ritschl, Ludwig [Ziehm Imaging GmbH, Donaustraße 31, 90451 Nürnberg (Germany); Sawall, Stefan; Kachelrieß, Marc [Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany and Institute of Medical Physics, Friedrich–Alexander–University (FAU) of Erlangen–Nürnberg, Henkestraße 91, 91052 Erlangen (Germany)

    2014-02-15

    Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. Methods: The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. Results: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. Conclusions: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.

  16. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.

    Science.gov (United States)

    Kohonen, Pekka; Parkkinen, Juuso A; Willighagen, Egon L; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C

    2017-07-03

    Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 10(8) data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

  17. Applicability of a gene expression based prediction method to SD and Wistar rats: an example of CARCINOscreen®.

    Science.gov (United States)

    Matsumoto, Hiroshi; Saito, Fumiyo; Takeyoshi, Masahiro

    2015-12-01

    Recently, the development of several gene expression-based prediction methods has been attempted in the fields of toxicology. CARCINOscreen® is a gene expression-based screening method to predict carcinogenicity of chemicals which target the liver with high accuracy. In this study, we investigated the applicability of the gene expression-based screening method to SD and Wistar rats by using CARCINOscreen®, originally developed with F344 rats, with two carcinogens, 2,4-diaminotoluen and thioacetamide, and two non-carcinogens, 2,6-diaminotoluen and sodium benzoate. After the 28-day repeated dose test was conducted with each chemical in SD and Wistar rats, microarray analysis was performed using total RNA extracted from each liver. Obtained gene expression data were applied to CARCINOscreen®. Predictive scores obtained by the CARCINOscreen® for known carcinogens were > 2 in all strains of rats, while non-carcinogens gave prediction scores below 0.5. These results suggested that the gene expression based screening method, CARCINOscreen®, can be applied to SD and Wistar rats, widely used strains in toxicological studies, by setting of an appropriate boundary line of prediction score to classify the chemicals into carcinogens and non-carcinogens.

  18. Improved gene prediction by principal component analysis based autoregressive Yule-Walker method.

    Science.gov (United States)

    Roy, Manidipa; Barman, Soma

    2016-01-10

    Spectral analysis using Fourier techniques is popular with gene prediction because of its simplicity. Model-based autoregressive (AR) spectral estimation gives better resolution even for small DNA segments but selection of appropriate model order is a critical issue. In this article a technique has been proposed where Yule-Walker autoregressive (YW-AR) process is combined with principal component analysis (PCA) for reduction in dimensionality. The spectral peaks of DNA signal are used to detect protein-coding regions based on the 1/3 frequency component. Here optimal model order selection is no more critical as noise is removed by PCA prior to power spectral density (PSD) estimation. Eigenvalue-ratio is used to find the threshold between signal and noise subspaces for data reduction. Superiority of proposed method over fast Fourier Transform (FFT) method and autoregressive method combined with wavelet packet transform (WPT) is established with the help of receiver operating characteristics (ROC) and discrimination measure (DM) respectively.

  19. Short communication: genetic variability in the predicted microRNA target sites of caprine casein genes.

    Science.gov (United States)

    Zidi, A; Amills, M; Tomás, A; Vidal, O; Ramírez, O; Carrizosa, J; Urrutia, B; Serradilla, J M; Clop, A

    2010-04-01

    The main goal of the current work was to identify single nucleotide polymorphisms (SNP) that might create or disrupt microRNA (miRNA) target sites in the caprine casein genes. The 3' untranslated regions of the goat alpha(S1)-, alpha(S2)-, beta-, and kappa-casein genes (CSN1S1, CSN1S2, CSN2, and CSN3, respectively) were resequenced in 25 individuals of the Murciano-Granadina, Cashmere, Canarian, Saanen, and Sahelian breeds. Five SNP were identified through this strategy: c.175C>T at CSN1S1; c.109T>C, c.139G>C, and c.160T>C at CSN1S2; and c.216C>T at CSN2. Analysis with the Patrocles Finder tool predicted that all of these SNP are located within regions complementary to the seed of diverse miRNA sequences. These in silico results suggest that polymorphism at miRNA target sites might have some effect on casein expression. We explored this issue by genotyping the c.175C>T SNP (CSN1S1) in 85 Murciano-Granadina goats with records for milk CSN1S1 concentrations. This substitution destroys a putative target site for miR-101, a miRNA known to be expressed in the bovine mammary gland. Although TT goats had higher levels (6.25 g/L) of CSN1S1 than their CT (6.05 g/L) and CC (6.04 g/L) counterparts, these differences were not significant. Experimental confirmation of the miRNA target sites predicted in the current work and performance of additional association analyses in other goat populations will be an essential step to find out if polymorphic miRNA target sites constitute an important source of variation in casein expression.

  20. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

    Directory of Open Access Journals (Sweden)

    Meysam Bastani

    Full Text Available 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.

  1. 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

  2. Polymorphisms in genes involved in EGFR-turnover are predictive for cetuximab efficacy in colorectal cancer

    Science.gov (United States)

    Stintzing, Sebastian; Zhang, Wu; Heinemann, Volker; Neureiter, Daniel; Kemmerling, Ralf; Kirchner, Thomas; Jung, Andreas; Folwaczny, Matthias; Yang, Dongyun; Ning, Yan; Sebio, Ana; Stremitzer, Stefan; Sunakawa, Yu; Matsusaka, Satoshi; Yamauchi, Shinichi; Loupakis, Fotios; Cremolini, Chiara; Falcone, Alfredo; Lenz, Heinz-Josef

    2015-01-01

    Transmembrane receptors such as the epidermal growth factor receptor (EGFR) are regulated by their turnover, which is dependent on the ubiquitin-proteasome-system (UPS). We tested in two independent study cohorts whether single nucleotide polymorphisms (SNPs) in genes involved in EGFR turnover predict clinical outcome in cetuximab treated metastatic colorectal cancer patients. The following SNPs involved in EGFR degradation were analyzed in a screening cohort of 108 patients treated with cetuximab in the chemorefractory setting: c-CBL (rs7105971; rs4938637; rs4938638; rs251837), EPS15 (rs17567; rs7308; rs1065754), NAE1 (rs363169; rs363170; rs363172); SH3KBP1 (rs7051590; rs5955820; rs1017874; rs11795873); SGIP1 (rs604737; rs6570808; rs7526812); UBE2M (rs895364; rs895374); UBE2L3 (rs5754216). SNPs showing an association with response or survival were analyzed in BRAF and RAS wild-type samples from the FIRE-3 study. 153 FOLFIRI plus cetuximab treated patients served as validation set, 168 patients of the FOLFIRI plus bevacizumab arm served as controls. EGFR FISH was done in 138 samples to test whether significant SNPs were associated with EGFR expression. UBE2M rs895374 was significantly associated with PFS (logrank-p = 0.005; HR 0.60) within cetuximab treated patients. No association with bevacizumab treated patients (n=168) could be established (p= 0.56, HR: 0.90). rs895374 genotype did not affect EGFR FISH measurements. EGFR recycling is an interesting mechanism of secondary resistance to cetuximab in mCRC. This is the first report suggesting that germline polymorphisms in the degradation process predict efficacy of cetuximab in patients with mCRC. Genes involved in EGFR turnover may be new targets in the treatment of mCRC. PMID:26206335

  3. Kindergarten Children's Perceptions of "Anthropomorphic Artifacts" with Adaptive Behavior

    Science.gov (United States)

    Kuperman, Asi; Mioduser, David

    2012-01-01

    In recent years, children from a kindergarten in central Israel have been exposed to learning experiences in technology as part of the implementation of a curriculum based on technological thinking, including topics related to behaving-adaptive-artifacts (e.g., robots). This study aims to unveil children's stance towards behaving artifacts:…

  4. 78 FR 4878 - Arts and Artifacts Indemnity Panel Advisory Committee

    Science.gov (United States)

    2013-01-23

    ... From the Federal Register Online via the Government Publishing Office NATIONAL FOUNDATION ON THE ARTS AND THE HUMANITIES Arts and Artifacts Indemnity Panel Advisory Committee AGENCY: Federal Council... Artifacts Domestic Indemnity Panel. The purpose of the meeting is for panel review, discussion,...

  5. 76 FR 25378 - Arts and Artifacts Indemnity Panel Advisory; Committee

    Science.gov (United States)

    2011-05-04

    ... From the Federal Register Online via the Government Publishing Office THE NATIONAL FOUNDATION ON THE ARTS AND THE HUMANITIES Federal Council on the Arts and the Humanities Arts and Artifacts...) notice is hereby given that a meeting of the Arts and Artifacts Indemnity Panel of the Federal Council...

  6. Implementing a Smart Method to Eliminate Artifacts of Vital Signals

    Directory of Open Access Journals (Sweden)

    Javadpour A.1

    2015-12-01

    Full Text Available Background: Electroencephalography (EEG has vital and significant applications in different medical fields and is used for the primary evaluation of neurological disorders. Hence, having easy access to suitable and useful signal is very important. Artifacts are undesirable confusions which are generally originated from inevitable human activities such as heartbeat, blinking of eyes and facial muscle activities while receiving EEG signal. It can bring about deformation in these waves though. Objective: The objective of this study was to find a suitable solution to eliminate the artifacts of Vital Signals. Methods: In this study, wavelet transform technique was used. This method is compared with threshold level. The threshold intensity is efficiently crucial because it should not remove the original signal instead of artifacts, and does not hold artifact signal instead of original ones. In this project, we seek to find and implement the algorithm with the ability to automatically remove the artifacts in EEG signals. For this purpose, the use of adaptive filtering methods such as wavelet analysis is appropriate. Finally, we observed that Functional Link Neural Network (FLN performance is better than ANFIS and RBFN to remove such artifacts. Results: We offer an intelligent method for removing artifacts from vital signals in neurological disorders. Conclusion: The proposed method can obtain more accurate results by removing artifacts of vital signals and can be useful in the early diagnosis of neurological and cardiovascular disorders

  7. Teaching and learning the nature of technical artifacts

    NARCIS (Netherlands)

    Frederik, I.; Sonneveld, W.; De Vries, M.J.

    2010-01-01

    Artifacts are probably our most obvious everyday encounter with technology. Therefore, a good understanding of the nature of technical artifacts is a relevant part of technological literacy. In this article we draw from the philosophy of technology to develop a conceptualization of technical artifac

  8. Incidental ferumoxytol artifacts in clinical brain MR imaging

    Energy Technology Data Exchange (ETDEWEB)

    Bowser, Bruce A.; Campeau, Norbert G.; Carr, Carrie M.; Diehn, Felix E.; McDonald, Jennifer S.; Miller, Gary M.; Kaufmann, Timothy J. [Mayo Clinic, Department of Radiology, Rochester, MN (United States)

    2016-11-15

    Ferumoxytol (Feraheme) is a parenteral therapy approved for treatment of iron deficiency anemia. The product insert for ferumoxytol states that it may affect the diagnostic ability of MRI for up to 3 months. However, the expected effects may not be commonly recognized among clinical neuroradiologists. Our purpose is to describe the artifacts we have seen at our institution during routine clinical practice. We reviewed the patients at our institution that had brain MRI performed within 90 days of receiving intravenous ferumoxytol. The imaging was reviewed for specific findings, including diffusion-weighted imaging vascular susceptibility artifact, gradient-echo echo-planar T2*-weighted vascular susceptibility artifact, SWI/SWAN vascular susceptibility artifact, hypointense vascular signal on T2-weighted images, pre-gadolinium contrast vascular enhancement on magnetization-prepared rapid acquisition gradient echo (MPRAGE) imaging, and effects on post-gadolinium contrast T1 imaging. Multiple artifacts were observed in patients having a brain MRI within 3 days of receiving intravenous ferumoxytol. These included susceptibility artifact on DWI, GRE, and SWAN/SWI imaging, pre-gadolinium contrast increased vascular signal on MPRAGE imaging, and decreased expected enhancement on post-gadolinium contrast T1-weighted imaging. Ferumoxytol can create imaging artifacts which complicate clinical interpretation when brain MRI is performed within 3 days of administration. Recognition of the constellation of artifacts produced by ferumoxytol is important in order to obviate additional unnecessary examinations and mitigate errors in interpretation. (orig.)

  9. Naturalistic Experience and the Early Use of Symbolic Artifacts

    Science.gov (United States)

    Troseth, Georgene L.; Casey, Amy M.; Lawver, Kelly A.; Walker, Joan M. T.; Cole, David A.

    2007-01-01

    Experience with a variety of symbolic artifacts has been proposed as a mechanism underlying symbolic development. In this study, the parents of 120 2-year-old children who participated in symbolic object retrieval tasks completed a questionnaire regarding their children's naturalistic experience with symbolic artifacts and activities. In separate…

  10. Magnetic resonance imaging susceptibility artifacts due to metallic foreign bodies.

    Science.gov (United States)

    Hecht, Silke; Adams, William H; Narak, Jill; Thomas, William B

    2011-01-01

    Susceptibility artifacts due to metallic foreign bodies may interfere with interpretation of magnetic resonance (MR) imaging studies. Additionally, migration of metallic objects may pose a risk to patients undergoing MR imaging. Our purpose was to investigate prevalence, underlying cause, and diagnostic implications of susceptibility artifacts in small animal MR imaging and report associated adverse effects. MR imaging studies performed in dogs and cats between April 2008 and March 2010 were evaluated retrospectively for the presence of susceptibility artifacts associated with metallic foreign bodies. Studies were performed using a 1.0 T scanner. Severity of artifacts was graded as 0 (no interference with area of interest), 1 (extension of artifact to area of interest without impairment of diagnostic quality), 2 (impairment of diagnostic quality but diagnosis still possible), or 3 (severe involvement of area of interest resulting in nondiagnostic study). Medical records were evaluated retrospectively to identify adverse effects. Susceptibility artifacts were present in 99/754 (13.1%) of MR imaging studies and were most common in examinations of the brachial plexus, thorax, and cervical spine. Artifacts were caused by identification microchips, ballistic fragments, skin staples/suture material, hemoclips, an ameroid constrictor, and surgical hardware. Three studies were nondiagnostic due to the susceptibility artifact. Adverse effects were not documented.

  11. Metal and calcification artifact reduction for digital breast tomosynthesis

    Science.gov (United States)

    Wicklein, Julia; Jerebko, Anna; Ritschl, Ludwig; Mertelmeier, Thomas

    2017-03-01

    Tomosynthesis images of the breast suffer from artifacts caused by the presence of highly absorbing materials. These can be either induced by metal objects like needles or clips inserted during biopsy devices, or larger calcifications inside the examined breast. Mainly two different kinds of artifacts appear after the filtered backprojection procedure. The first type is undershooting artifacts near edges of high-contrast objects caused by the filtering step. The second type is out-of-plane (ripple) artifacts that appear even in slices where the metal object or macrocalcifications does not exist. Due to the limited angular range of tomosynthesis systems, overlapping structures have high influence on neighboring regions. To overcome these problems, a segmentation of artifact introducing objects is performed on the projection images. Both projection versions, with and without high-contrast objects are filtered independently to avoid undershootings. During backprojection a decision is made for each reconstructed voxel, if it is artifact or high-contrast object. This is based on a mask image, gained from the segmentation of high-contrast objects. This procedure avoids undershooting artifacts and additionally reduces out-of-plane ripple. Results are demonstrated for different kinds of artifact inducing objects and calcifications.

  12. An Eighteen-Gene Classifier Predicts Locoregional Recurrence in Post-Mastectomy Breast Cancer Patients

    Directory of Open Access Journals (Sweden)

    Skye H. Cheng

    2016-03-01

    Full Text Available We previously identified 34 genes of interest (GOI in 2006 to aid the oncologists to determine whether post-mastectomy radiotherapy (PMRT is indicated for certain patients with breast cancer. At this time, an independent cohort of 135 patients having DNA microarray study available from the primary tumor tissue samples was chosen. Inclusion criteria were 1 mastectomy as the first treatment, 2 pathology stages I-III, 3 any locoregional recurrence (LRR and 4 no PMRT. After inter-platform data integration of Affymetrix U95 and U133 Plus 2.0 arrays and quantile normalization, in this paper we used 18 of 34 GOI to divide the mastectomy patients into high and low risk groups. The 5-year rate of freedom from LRR in the high-risk group was 30%. In contrast, in the low-risk group it was 99% (p<0.0001. Multivariate analysis revealed that the 18-gene classifier independently predicts rates of LRR regardless of nodal status or cancer subtype.

  13. Xenobiotic metabolizing enzyme gene polymorphisms predict response to lung volume reduction surgery

    Directory of Open Access Journals (Sweden)

    DeMeo Dawn L

    2007-08-01

    Full Text Available Abstract Background In the National Emphysema Treatment Trial (NETT, marked variability in response to lung volume reduction surgery (LVRS was observed. We sought to identify genetic differences which may explain some of this variability. Methods In 203 subjects from the NETT Genetics Ancillary Study, four outcome measures were used to define response to LVRS at six months: modified BODE index, post-bronchodilator FEV1, maximum work achieved on a cardiopulmonary exercise test, and University of California, San Diego shortness of breath questionnaire. Sixty-four single nucleotide polymorphisms (SNPs were genotyped in five genes previously shown to be associated with chronic obstructive pulmonary disease susceptibility, exercise capacity, or emphysema distribution. Results A SNP upstream from glutathione S-transferase pi (GSTP1; p = 0.003 and a coding SNP in microsomal epoxide hydrolase (EPHX1; p = 0.02 were each associated with change in BODE score. These effects appeared to be strongest in patients in the non-upper lobe predominant, low exercise subgroup. A promoter SNP in EPHX1 was associated with change in BODE score (p = 0.008, with the strongest effects in patients with upper lobe predominant emphysema and low exercise capacity. One additional SNP in GSTP1 and three additional SNPs in EPHX1 were associated (p Conclusion Genetic variants in GSTP1 and EPHX1, two genes encoding xenobiotic metabolizing enzymes, were predictive of response to LVRS. These polymorphisms may identify patients most likely to benefit from LVRS.

  14. Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes.

    Science.gov (United States)

    Li, Yi; Sulik, Michael J; Eisenberg, Nancy; Spinrad, Tracy L; Lemery-Chalfant, Kathryn; Stover, Daryn A; Verrelli, Brian C

    2016-02-01

    Children's observed effortful control (EC) at 30, 42, and 54 months (n = 145) was predicted from the interaction between mothers' observed parenting with their 30-month-olds and three variants of the solute carrier family C6, member 3 (SLC6A3) dopamine transporter gene (single nucleotide polymorphisms in intron8 and intron13, and a 40 base pair variable number tandem repeat [VNTR] in the 3'-untranslated region [UTR]), as well as haplotypes of these variants. Significant moderating effects were found. Children without the intron8-A/intron13-G, intron8-A/3'-UTR VNTR-10, or intron13-G/3'-UTR VNTR-10 haplotypes (i.e., haplotypes associated with the reduced SLC6A3 gene expression and thus lower dopamine functioning) appeared to demonstrate altered levels of EC as a function of maternal parenting quality, whereas children with these haplotypes demonstrated a similar EC level regardless of the parenting quality. Children with these haplotypes demonstrated a trade-off, such that they showed higher EC, relative to their counterparts without these haplotypes, when exposed to less supportive maternal parenting. The findings revealed a diathesis-stress pattern and suggested that different SLC6A3 haplotypes, but not single variants, might represent different levels of young children's sensitivity/responsivity to early parenting.

  15. Multiple genetic interaction experiments provide complementary information useful for gene function prediction.

    Directory of Open Access Journals (Sweden)

    Magali Michaut

    Full Text Available Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

  16. Interactions of adolescent social experiences and dopamine genes to predict physical intimate partner violence perpetration

    Science.gov (United States)

    Parker, Edith A.; Peek-Asa, Corinne

    2017-01-01

    Objectives We examined the interactions between three dopamine gene alleles (DAT1, DRD2, DRD4) previously associated with violent behavior and two components of the adolescent environment (exposure to violence, school social environment) to predict adulthood physical intimate partner violence (IPV) perpetration among white men and women. Methods We used data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health, a cohort study following individuals from adolescence to adulthood. Based on the prior literature, we categorized participants as at risk for each of the three dopamine genes using this coding scheme: two 10-R alleles for DAT1; at least one A-1 allele for DRD2; at least one 7-R or 8-R allele for DRD4. Adolescent exposure to violence and school social environment was measured in 1994 and 1995 when participants were in high school or middle school. Intimate partner violence perpetration was measured in 2008 when participants were 24 to 32 years old. We used simple and multivariable logistic regression models, including interactions of genes and the adolescent environments for the analysis. Results Presence of risk alleles was not independently associated with IPV perpetration but increasing exposure to violence and disconnection from the school social environment was associated with physical IPV perpetration. The effects of these adolescent experiences on physical IPV perpetration varied by dopamine risk allele status. Among individuals with non-risk dopamine alleles, increased exposure to violence during adolescence and perception of disconnection from the school environment were significantly associated with increased odds of physical IPV perpetration, but individuals with high risk alleles, overall, did not experience the same increase. Conclusion Our results suggested the effects of adolescent environment on adulthood physical IPV perpetration varied by genetic factors. This analysis did not find a direct link between risk alleles

  17. New Blocking Artifacts Reduction Method Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    SHI Min; YI Qing-ming

    2007-01-01

    It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The algorithm exploits blocking-artifact features shown in wavelet domain. The energy of blocking artifacts is concentrated into some lines to form annoying visual effects after wavelet transform. The aim of reducing blocking artifacts is to capture excessive energy on the block boundary effectively and reduce it below the visual scope. Adaptive operators for different subbands are computed based on the wavelet coefficients. The operators are made adaptive to different images and characteristics of blocking artifacts. Experimental results show that the proposed method can significantly improve the visual quality and also increase the peak signal-noise-ratio(PSNR) in the output image.

  18. Distributed Cognition and Distributed Morality: Agency, Artifacts and Systems.

    Science.gov (United States)

    Heersmink, Richard

    2017-04-01

    There are various philosophical approaches and theories describing the intimate relation people have to artifacts. In this paper, I explore the relation between two such theories, namely distributed cognition and distributed morality theory. I point out a number of similarities and differences in these views regarding the ontological status they attribute to artifacts and the larger systems they are part of. Having evaluated and compared these views, I continue by focussing on the way cognitive artifacts are used in moral practice. I specifically conceptualise how such artifacts (a) scaffold and extend moral reasoning and decision-making processes, (b) have a certain moral status which is contingent on their cognitive status, and (c) whether responsibility can be attributed to distributed systems. This paper is primarily written for those interested in the intersection of cognitive and moral theory as it relates to artifacts, but also for those independently interested in philosophical debates in extended and distributed cognition and ethics of (cognitive) technology.

  19. Prediction of Enhancement Effect of Nitroimidazoles on Irradiation by Gene Expression Programming

    Institute of Scientific and Technical Information of China (English)

    LONG Wei; ZHANG Xiao-dong; WANG Hao; SHEN Xiu; SI Hong-zong; FAN Sai-jun; ZHOU Ze-wei

    2013-01-01

    A novel machine learning method,gene expression programming(GEP),was employed to build quatitative structure-activity relationship(QSAR) models for predicting the enhancement effect of nitroimidazole compounds on irradiation.The models were based on descriptors which were calculated from the molecular structures.Four descriptors were selected from the pool of descriptors by best multiple linear regression(BMLR) method.After that,three regression methods,multiple linear regression(MLR),support vector machine(SVM) and GEP,were used to build QSAR models.Compared to MLR and SVM,GEP produced a better model with the square of correlation coefficient(R2),0.9203 and 0.9014,and the root mean square error(RMSE),0.6187 and 0.6875,for training set and test set,respectively.The results show that the GEP model has better predictive ability and more reliable than the MLR and SVM models.This indicates that GEP is a promising method on relevant researches in radiation area.

  20. Melanopsin gene variations interact with season to predict sleep onset and chronotype.

    Science.gov (United States)

    Roecklein, Kathryn A; Wong, Patricia M; Franzen, Peter L; Hasler, Brant P; Wood-Vasey, W Michael; Nimgaonkar, Vishwajit L; Miller, Megan A; Kepreos, Kyle M; Ferrell, Robert E; Manuck, Stephen B

    2012-10-01

    The human melanopsin gene has been reported to mediate risk for seasonal affective disorder (SAD), which is hypothesized to be caused by decreased photic input during winter when light levels fall below threshold, resulting in differences in circadian phase and/or sleep. However, it is unclear if melanopsin increases risk of SAD by causing differences in sleep or circadian phase, or if those differences are symptoms of the mood disorder. To determine if melanopsin sequence variations are associated with differences in sleep-wake behavior among those not suffering from a mood disorder, the authors tested associations between melanopsin gene polymorphisms and self-reported sleep timing (sleep onset and wake time) in a community sample (N = 234) of non-Hispanic Caucasian participants (age 30-54 yrs) with no history of psychological, neurological, or sleep disorders. The authors also tested the effect of melanopsin variations on differences in preferred sleep and activity timing (i.e., chronotype), which may reflect differences in circadian phase, sleep homeostasis, or both. Daylength on the day of assessment was measured and included in analyses. DNA samples were genotyped for melanopsin gene polymorphisms using fluorescence polarization. P10L genotype interacted with daylength to predict self-reported sleep onset (interaction p sleep onset among those with the TT genotype was later in the day when individuals were assessed on longer days and earlier in the day on shorter days, whereas individuals in the other genotype groups (i.e., CC and CT) did not show this interaction effect. P10L genotype also interacted in an analogous way with daylength to predict self-reported morningness (interaction p sleep onset and chronotype as a function of daylength, whereas other genotypes at P10L do not seem to have effects that vary by daylength. A better understanding of how melanopsin confers heightened responsivity to daylength may improve our understanding of a broad range of

  1. Body MR Imaging: Artifacts, k-Space, and Solutions.

    Science.gov (United States)

    Huang, Susie Y; Seethamraju, Ravi T; Patel, Pritesh; Hahn, Peter F; Kirsch, John E; Guimaraes, Alexander R

    2015-01-01

    Body magnetic resonance (MR) imaging is challenging because of the complex interaction of multiple factors, including motion arising from respiration and bowel peristalsis, susceptibility effects secondary to bowel gas, and the need to cover a large field of view. The combination of these factors makes body MR imaging more prone to artifacts, compared with imaging of other anatomic regions. Understanding the basic MR physics underlying artifacts is crucial to recognizing the trade-offs involved in mitigating artifacts and improving image quality. Artifacts can be classified into three main groups: (a) artifacts related to magnetic field imperfections, including the static magnetic field, the radiofrequency (RF) field, and gradient fields; (b) artifacts related to motion; and (c) artifacts arising from methods used to sample the MR signal. Static magnetic field homogeneity is essential for many MR techniques, such as fat saturation and balanced steady-state free precession. Susceptibility effects become more pronounced at higher field strengths and can be ameliorated by using spin-echo sequences when possible, increasing the receiver bandwidth, and aligning the phase-encoding gradient with the strongest susceptibility gradients, among other strategies. Nonuniformities in the RF transmit field, including dielectric effects, can be minimized by applying dielectric pads or imaging at lower field strength. Motion artifacts can be overcome through respiratory synchronization, alternative k-space sampling schemes, and parallel imaging. Aliasing and truncation artifacts derive from limitations in digital sampling of the MR signal and can be rectified by adjusting the sampling parameters. Understanding the causes of artifacts and their possible solutions will enable practitioners of body MR imaging to meet the challenges of novel pulse sequence design, parallel imaging, and increasing field strength.

  2. Peripheral neuropathy predicts nuclear gene defect in patients with mitochondrial ophthalmoplegia.

    Science.gov (United States)

    Horga, Alejandro; Pitceathly, Robert D S; Blake, Julian C; Woodward, Catherine E; Zapater, Pedro; Fratter, Carl; Mudanohwo, Ese E; Plant, Gordon T; Houlden, Henry; Sweeney, Mary G; Hanna, Michael G; Reilly, Mary M

    2014-12-01

    Progressive external ophthalmoplegia is a common clinical feature in mitochondrial disease caused by nuclear DNA defects and single, large-scale mitochondrial DNA deletions and is less frequently associated with point mutations of mitochondrial DNA. Peripheral neuropathy is also a frequent manifestation of mitochondrial disease, although its prevalence and characteristics varies considerably among the different syndromes and genetic aetiologies. Based on clinical observations, we systematically investigated whether the presence of peripheral neuropathy could predict the underlying genetic defect in patients with progressive external ophthalmoplegia. We analysed detailed demographic, clinical and neurophysiological data from 116 patients with genetically-defined mitochondrial disease and progressive external ophthalmoplegia. Seventy-eight patients (67%) had a single mitochondrial DNA deletion, 12 (10%) had a point mutation of mitochondrial DNA and 26 (22%) had mutations in either POLG, C10orf2 or RRM2B, or had multiple mitochondrial DNA deletions in muscle without an identified nuclear gene defect. Seventy-seven patients had neurophysiological studies; of these, 16 patients (21%) had a large-fibre peripheral neuropathy. The prevalence of peripheral neuropathy was significantly lower in patients with a single mitochondrial DNA deletion (2%) as compared to those with a point mutation of mitochondrial DNA or with a nuclear DNA defect (44% and 52%, respectively; Pneuropathy as the only independent predictor associated with a nuclear DNA defect (P=0.002; odds ratio 8.43, 95% confidence interval 2.24-31.76). Multinomial logistic regression analysis identified peripheral neuropathy, family history and hearing loss as significant predictors of the genotype, and the same three variables showed the highest performance in genotype classification in a decision tree analysis. Of these variables, peripheral neuropathy had the highest specificity (91%), negative predictive value

  3. Gene Expression Versus Sequence for Predicting Function:Glia Maturation Factor Gamma Is Not A Glia Maturation Factor

    Institute of Scientific and Technical Information of China (English)

    MichaelG.Walker

    2003-01-01

    It is standard practice,whenever a researcher finds a new gene,to search databases for genes that have a similar sequence.It is not standard practice,whenever a researcher finds a new gene,to search for genes that have similar expression(coexpression).Failure to perform co-expression searches has lead to incorrect conclusions about the likely function of new genes,and has lead to wasted laboratory attempts to confirm functions incorrectly predicted.We present here the example of Glia Maturation Factor gamma(GMF-gamma).Despite its name,it has not been shown to participate in glia maturation.It is a gene of unknown function that is similar in sequence to GMF-beta.The sequence homology and chromosomal location led to an unsuccessful searchfor GMF-gamma mutations in glioma.We examined GMF-gamma expression in 1432 human cDNA libraries.Highest expression occurs in phagocytic,antigen-presenting and other hematopoietic cells.We found GMF-gamma mRNA in almost every tissue examined,with expression in nervous tissue no higher than in any other tissue.Our evidence indicates that GMF-gamma participates in phagocytosis in antigen presenting cells.Searches for genes with similar sequences should be supplemented with searches for genes with similar expression to avoid incorrect predictions.

  4. Gene Expression Versus Sequence for Predicting Function: Glia Maturation Factor Gamma Is Not A Glia Maturation Factor

    Institute of Scientific and Technical Information of China (English)

    Michael G. Walker

    2003-01-01

    It is standard practice, whenever a researcher finds a new gene, to search databases for genes that have a similar sequence. It is not standard practice, whenever a researcher finds a new gene, to search for genes that have similar expression (coexpression). Failure to perform co-expression searches has lead to incorrect conclusions about the likely function of new genes, and has lead to wasted laboratory attempts to confirm functions incorrectly predicted. We present here the example of Glia Maturation Factor gamma (GMF-gamma). Despite its name, it has not been shown to participate in glia maturation. It is a gene of unknown function that is similar in sequence to GMF-beta. The sequence homology and chromosomal location led to an unsuccessful search for GMF-gamma mutations in glioma.We examined GMF-gamma expression in 1432 human cDNA libraries. Highest expression occurs in phagocytic, antigen-presenting and other hematopoietic cells.We found GMF-gamma mRNA in almost every tissue examined, with expression in nervous tissue no higher than in any other tissue. Our evidence indicates that GMF-gamma participates in phagocytosis in antigen presenting cells. Searches for genes with similar sequences should be supplemented with searches for genes with similar expression to avoid incorrect predictions.

  5. High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.

    Directory of Open Access Journals (Sweden)

    Wenting Li

    Full Text Available A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.

  6. Incidence of "quasi-ditags" in catalogs generated by Serial Analysis of Gene Expression (SAGE)

    Science.gov (United States)

    Anisimov, Sergey V; Sharov, Alexei A

    2004-01-01

    Background Serial Analysis of Gene Expression (SAGE) is a functional genomic technique that quantitatively analyzes the cellular transcriptome. The analysis of SAGE libraries relies on the identification of ditags from sequencing files; however, the software used to examine SAGE libraries cannot distinguish between authentic versus false ditags ("quasi-ditags"). Results We provide examples of quasi-ditags that originate from cloning and sequencing artifacts (i.e. genomic contamination or random combinations of nucleotides) that are included in SAGE libraries. We have employed a mathematical model to predict the frequency of quasi-ditags in random nucleotide sequences, and our data show that clones containing less than or equal to 2 ditags (which include chromosomal cloning artifacts) should be excluded from the analysis of SAGE catalogs. Conclusions Cloning and sequencing artifacts contaminating SAGE libraries could be eliminated using simple pre-screening procedure to increase the reliability of the data. PMID:15491492

  7. Incidence of "quasi-ditags" in catalogs generated by Serial Analysis of Gene Expression (SAGE

    Directory of Open Access Journals (Sweden)

    Sharov Alexei A

    2004-10-01

    Full Text Available Abstract Background Serial Analysis of Gene Expression (SAGE is a functional genomic technique that quantitatively analyzes the cellular transcriptome. The analysis of SAGE libraries relies on the identification of ditags from sequencing files; however, the software used to examine SAGE libraries cannot distinguish between authentic versus false ditags ("quasi-ditags". Results We provide examples of quasi-ditags that originate from cloning and sequencing artifacts (i.e. genomic contamination or random combinations of nucleotides that are included in SAGE libraries. We have employed a mathematical model to predict the frequency of quasi-ditags in random nucleotide sequences, and our data show that clones containing less than or equal to 2 ditags (which include chromosomal cloning artifacts should be excluded from the analysis of SAGE catalogs. Conclusions Cloning and sequencing artifacts contaminating SAGE libraries could be eliminated using simple pre-screening procedure to increase the reliability of the data.

  8. CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations.

    Science.gov (United States)

    Park, Julie; Costanzo, Maria C; Balakrishnan, Rama; Cherry, J Michael; Hong, Eurie L

    2012-01-01

    The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation. DATABASE URL: http://www.yeastgenome.org.

  9. Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer.

    Science.gov (United States)

    Wang, Haiying; Molina, Julian; Jiang, John; Ferber, Matthew; Pruthi, Sandhya; Jatkoe, Timothy; Derecho, Carlo; Rajpurohit, Yashoda; Zheng, Jian; Wang, Yixin

    2013-11-01

    Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and

  10. Expression of tumor necrosis factor-alpha-mediated genes predicts recurrence-free survival in lung cancer.

    Science.gov (United States)

    Wang, Baohua; Song, Ning; Yu, Tong; Zhou, Lianya; Zhang, Helin; Duan, Lin; He, Wenshu; Zhu, Yihua; Bai, Yunfei; Zhu, Miao

    2014-01-01

    In this study, we conducted a meta-analysis on high-throughput gene expression data to identify TNF-α-mediated genes implicated in lung cancer. We first investigated the gene expression profiles of two independent TNF-α/TNFR KO murine models. The EGF receptor signaling pathway was the top pathway associated with genes mediated by TNF-α. After matching the TNF-α-mediated mouse genes to their human orthologs, we compared the expression patterns of the TNF-α-mediated genes in normal and tumor lung tissues obtained from humans. Based on the TNF-α-mediated genes that were dysregulated in lung tumors, we developed a prognostic gene signature that effectively predicted recurrence-free survival in lung cancer in two validation cohorts. Resampling tests suggested that the prognostic power of the gene signature was not by chance, and multivariate analysis suggested that this gene signature was independent of the traditional clinical factors and enhanced the identification of lung cancer patients at greater risk for recurrence.

  11. Expression of tumor necrosis factor-alpha-mediated genes predicts recurrence-free survival in lung cancer.

    Directory of Open Access Journals (Sweden)

    Baohua Wang

    Full Text Available In this study, we conducted a meta-analysis on high-throughput gene expression data to identify TNF-α-mediated genes implicated in lung cancer. We first investigated the gene expression profiles of two independent TNF-α/TNFR KO murine models. The EGF receptor signaling pathway was the top pathway associated with genes mediated by TNF-α. After matching the TNF-α-mediated mouse genes to their human orthologs, we compared the expression patterns of the TNF-α-mediated genes in normal and tumor lung tissues obtained from humans. Based on the TNF-α-mediated genes that were dysregulated in lung tumors, we developed a prognostic gene signature that effectively predicted recurrence-free survival in lung cancer in two validation cohorts. Resampling tests suggested that the prognostic power of the gene signature was not by chance, and multivariate analysis suggested that this gene signature was independent of the traditional clinical factors and enhanced the identification of lung cancer patients at greater risk for recurrence.

  12. [Joint correction for motion artifacts and off-resonance artifacts in multi-shot diffusion magnetic resonance imaging].

    Science.gov (United States)

    Wu, Wenchuan; Fang, Sheng; Guo, Hua

    2014-06-01

    Aiming at motion artifacts and off-resonance artifacts in multi-shot diffusion magnetic resonance imaging (MRI), we proposed a joint correction method in this paper to correct the two kinds of artifacts simultaneously without additional acquisition of navigation data and field map. We utilized the proposed method using multi-shot variable density spiral sequence to acquire MRI data and used auto-focusing technique for image deblurring. We also used direct method or iterative method to correct motion induced phase errors in the process of deblurring. In vivo MRI experiments demonstrated that the proposed method could effectively suppress motion artifacts and off-resonance artifacts and achieve images with fine structures. In addition, the scan time was not increased in applying the proposed method.

  13. Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients.

    Science.gov (United States)

    Lu, Yan; Wang, Liang; Liu, Pengyuan; Yang, Ping; You, Ming

    2012-01-01

    About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset -142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = -0.83, P85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.

  14. Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients.

    Directory of Open Access Journals (Sweden)

    Yan Lu

    Full Text Available About 30% stage I non-small cell lung cancer (NSCLC patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset -142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = -0.83, P85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.

  15. Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow.

    Science.gov (United States)

    van Strien, Maarten J; Keller, Daniela; Holderegger, Rolf; Ghazoul, Jaboury; Kienast, Felix; Bolliger, Janine

    2014-03-01

    For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (landscape planning.

  16. Searching for alien artifacts on the moon

    Science.gov (United States)

    Davies, P. C. W.; Wagner, R. V.

    2013-08-01

    The Search for Extraterrestrial Intelligence (SETI) has a low probability of success, but it would have a high impact if successful. Therefore it makes sense to widen the search as much as possible within the confines of the modest budget and limited resources currently available. To date, SETI has been dominated by the paradigm of seeking deliberately beamed radio messages. However, indirect evidence for extraterrestrial intelligence could come from any incontrovertible signatures of non-human technology. Existing searchable databases from astronomy, biology, earth and planetary sciences all offer low-cost opportunities to seek a footprint of extraterrestrial technology. In this paper we take as a case study one particular new and rapidly-expanding database: the photographic mapping of the Moon's surface by the Lunar Reconnaissance Orbiter (LRO) to 0.5 m resolution. Although there is only a tiny probability that alien technology would have left traces on the moon in the form of an artifact or surface modification of lunar features, this location has the virtue of being close, and of preserving traces for an immense duration. Systematic scrutiny of the LRO photographic images is being routinely conducted anyway for planetary science purposes, and this program could readily be expanded and outsourced at little extra cost to accommodate SETI goals, after the fashion of the SETI@home and Galaxy Zoo projects.

  17. Method for eliminating artifacts in CCD imagers

    Science.gov (United States)

    Turko, B. T.; Yates, G. J.

    1990-06-01

    An electronic method for eliminating artifacts in a video camera employing a charge coupled device (CCD) as an image sensor is presented. The method comprises the step of initializing the camera prior to normal readout. The method includes a first dump cycle period for transferring radiation generated charge into the horizontal register. This occurs while the decaying image on the phosphor being imaged is being integrated in the photosites, and a second dump cycle period, occurring after the phosphor image has decayed, for rapidly dumping unwanted smear charge which has been generated in the vertical registers. Image charge is then transferred from the photosites and to the vertical registers and readout in conventional fashion. The inventive method allows the video camera to be used in environments having high ionizing radiation content, and to capture images of events of very short duration and occurring either within or outside the normal visual wavelength spectrum. Resultant images are free from ghost, smear, and smear phenomena caused by insufficient opacity of the registers, and are also free from random damage caused by ionization charges which exceed the charge limit capacity of the photosites.

  18. The specific contribution of object's origin on artifacts categorization

    Institute of Scientific and Technical Information of China (English)

    SUN Yuhao; WANG Zhe; FU Xiaolan

    2006-01-01

    Gelman and Bloom found that adults and children's object naming was sensitive to how an object was created (man-made or not), but they did not reveal on which specific level of conceptual system this effect was. Using a free-naming task and a force-choice task, two experiments were conducted to test a hypothesis that this effect was specifically on domain level ("artifact/non-artifact" distinction). In Experiment 1, participants were asked to name shortly-depicted objects, rate their confidence, and report their reasons for each naming response. Results showed that most of the naming responses in "man-made" condition were in artifact domain, and most in "natural" condition were in non-artifact domain, although in both conditions names were very divergent on basic level. In Experiment 2, another group of participants were asked to choose one from two names (one in artifact domain and the other in non-artifact domain) to match the same shortly-depicted objects presented in the first experiment. Results of Experiment 1 on domain level were replicated in Experiment 2. These convergent findings supported the hypothesis that the effect of object's origin is specifically on domain level of conceptual system of objects. Reasons explicitly reported for naming responses in Experiment 1 suggested that participants might automatically infer objects' functions in "man-made" condition but not in "natural" condition.Here the function-based hypothesis of artifacts classification is discussed.

  19. Towards discrimination of infarcts from artifacts in DWI scans

    Energy Technology Data Exchange (ETDEWEB)

    Gupta, Varsha; Prakash, K.N.B.; Nowinski, Wieslaw L. [Technology and Research, Biomedical Imaging Lab, Agency for Science, Singapore (Singapore)

    2008-04-15

    Accurate and rapid quantification of infarcts from DWI scans is critical in acute ischemic stroke. Acquisition artifacts lead to hyperintense regions in DWI MR scans resulting in false positives. Discriminating infarcts and artifacts helps in reducing infarct segmentation errors. An algorithm based on two-dimensional symmetry of artifacts about the midsagittal plane and three-dimensional spatial coherence of infarct regions is proposed to characterize and separate infarcts from artifacts. The two dimensional symmetry is quantified by propagating Poisson errors in the intensity space of each pixel, and distant and spatially incoherent regions in a volume are considered as artifacts. The combination of two criteria enhances the confidence in the decision whether a hyperintense region is an infarct or artifact. The validity of the proposed algorithm is demonstrated using 51 cases. The improvement in results is demonstrated in three situations: (1) automatic infarct slice identification resulting in an average increase in (specificity, Dice Statistical Index (DSI)) by (15.2%, 6.9%) while the sensitivity decrease is by only 1.5% and (2) automatic infarct segmentation using two different algorithms: first resulting in an average DSI increase by 7.6% and second by 5.1%. On a matlab platform, the processing time is < 1 s. The proposed algorithm is useful as a fast post-processing tool to reduce artifacts in infarct processing applications. (orig.)

  20. Sex-linkage of sexually antagonistic genes is predicted by female, but not male, effects in birds.

    Science.gov (United States)

    Mank, Judith E; Ellegren, Hans

    2009-06-01

    Evolutionary theory predicts that sexually antagonistic loci will be preferentially sex-linked, and this association can be empirically testes with data on sex-biased gene expression with the assumption that sex-biased gene expression represents the resolution of past sexual antagonism. However, incomplete dosage compensating mechanisms and meiotic sex chromosome inactivation have hampered efforts to connect expression data to theoretical predictions regarding the genomic distribution of sexually antagonistic loci in a variety of animals. Here we use data on the underlying regulatory mechanism that produce expression sex-bias to test the genomic distribution of sexually antagonistic genes in chicken. Using this approach, which is free from problems associated with the lack of dosage compensation in birds, we show that female-detriment genes are significantly overrepresented on the Z chromosome, and female-benefit genes underrepresented. By contrast, male-effect genes show no over- or underrepresentation on the Z chromosome. These data are consistent with a dominant mode of inheritance for sexually antagonistic genes, in which male-benefit coding mutations are more likely to be fixed on the Z due to stronger male-specific selective pressures. After fixation of male-benefit alleles, regulatory changes in females evolve to minimize antagonism by reducing female expression.

  1. Adiabatic Low-Pass J Filters for Artifact Suppression in Heteronuclear NMR

    DEFF Research Database (Denmark)

    Meier, Sebastian; Benie, Andrew J; Duus, Jens Øllgaard

    2009-01-01

    NMR artifact purging: Modern NMR experiments depend on efficient coherence transfer pathways for their sensitivity and on suppression of undesired pathways leading to artifacts for their spectral clarity. A novel robust adiabatic element suppresses hard-to-get-at artifacts.......NMR artifact purging: Modern NMR experiments depend on efficient coherence transfer pathways for their sensitivity and on suppression of undesired pathways leading to artifacts for their spectral clarity. A novel robust adiabatic element suppresses hard-to-get-at artifacts....

  2. In Silico Analysis of Microarray-Based Gene Expression Profiles Predicts Tumor Cell Response to Withanolides

    Directory of Open Access Journals (Sweden)

    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.

  3. Multifactorial Patterns of Gene Expression in Colonic Epithelial Cells Predict Disease Phenotypes in Experimental Colitis

    Science.gov (United States)

    Frantz, Aubrey L.; Bruno, Maria E.C.; Rogier, Eric W.; Tuna, Halide; Cohen, Donald A.; Bondada, Subbarao; Chelvarajan, R. Lakshman; Brandon, J. Anthony; Jennings, C. Darrell; Kaetzel, Charlotte S.

    2012-01-01

    Background The pathogenesis of inflammatory bowel disease (IBD) is complex and the need to identify molecular biomarkers is critical. Epithelial cells play a central role in maintaining intestinal homeostasis. We previously identified 5 “signature” biomarkers in colonic epithelial cells (CEC) that are predictive of disease phenotype in Crohn’s disease. Here we investigate the ability of CEC biomarkers to define the mechanism and severity of intestinal inflammation. Methods We analyzed expression of RelA, A20, pIgR, TNF and MIP-2 in CEC of mice with DSS acute colitis or T cell-mediated chronic colitis. Factor analysis was used to combine the 5 biomarkers into 2 multifactorial principal components (PCs). PC scores for individual mice were correlated with disease severity. Results For both colitis models, PC1 was strongly weighted toward RelA, A20 and pIgR, and PC2 was strongly weighted toward TNF and MIP-2, while the contributions of other biomarkers varied depending on the etiology of inflammation. Disease severity was correlated with elevated PC2 scores in DSS colitis and reduced PC1 scores in T cell transfer colitis. Down-regulation of pIgR was a common feature observed in both colitis models and was associated with altered cellular localization of pIgR and failure to transport IgA. Conclusions A multifactorial analysis of epithelial gene expression may be more informative than examining single gene responses in IBD. These results provide insight into the homeostatic and pro-inflammatory functions of CEC in IBD pathogenesis and suggest that biomarker analysis could be useful for evaluating therapeutic options for IBD patients. PMID:23070952

  4. A gene expression signature that can predict green tea exposure and chemopreventive efficacy of lung cancer in mice.

    Science.gov (United States)

    Lu, Yan; Yao, Ruisheng; Yan, Ying; Wang, Yian; Hara, Yukihiko; Lubet, Ronald A; You, Ming

    2006-02-15

    Green tea has been shown to be a potent chemopreventive agent against lung tumorigenesis in animal models. Previously, we found that treatment of A/J mice with either green tea (0.6% in water) or a defined green tea catechin extract (polyphenon E; 2.0 g/kg in diet) inhibited lung tumor tumorigenesis. Here, we described expression profiling of lung tissues derived from these studies to determine the gene expression signature that can predict the exposure and efficacy of green tea in mice. We first profiled global gene expressions in normal lungs versus lung tumors to determine genes which might be associated with the tumorigenic process (TUM genes). Gene expression in control tumors and green tea-treated tumors (either green tea or polyphenon E) were compared to determine those TUM genes whose expression levels in green tea-treated tumors returned to levels seen in normal lungs. We established a 17-gene expression profile specific for exposure to effective doses of either green tea or polyphenon E. This gene expression signature was altered both in normal lungs and lung adenomas when mice were exposed to green tea or polyphenon E. These experiments identified patterns of gene expressions that both offer clues for green tea's potential mechanisms of action and provide a molecular signature specific for green tea exposure.

  5. A POCS-Based Algorithm for Blocking Artifacts Reduction

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yi-hong; CHENG Guo-hua; YU Song-yu

    2006-01-01

    An algorithm for blocking artifacts reduction in DCT domain for block-based image coding was developed. The algorithm is based on the projection onto convex set (POCS) theory. Due to the fact that the DCT characteristics of shifted blocks are different caused by the blocking artifacts, a novel smoothness constraint set and the corresponding projection operator were proposed to reduce the blocking artifacts by discarding the undesired high frequency coefficients in the shifted DCT blocks. The experimental results show that the proposed algorithm outperforms the conventional algorithms in terms of objective quality, subjective quality, and convergence property.

  6. Artifacts interfering with interpretation of cone beam computed tomography images.

    Science.gov (United States)

    Makins, Scott R

    2014-07-01

    Artifacts in radiographic imaging are discrepancies between the reconstructed visual image and the content of the subject. In radiographic imaging, this means the grayscale values in the image do not accurately reflect the attenuation values of the subject. Structures may also appear that do not exist in the subject. Whatever the source or appearance of image artifacts, their presence degrades the accuracy of the image in relation to the true characteristics of the subject. One should therefore be aware of the presence of artifacts and be familiar with their characteristic appearances in order to enhance the extraction of diagnostic information.

  7. Towards a concept of community artifact ecology in HCI?

    DEFF Research Database (Denmark)

    Saad-Sulonen, Joanna; Korsgaard, Henrik

    or workplaces do. This has implications on understanding how to research and design HCI for communities but also on refining the ecological perspective in HCI. We look in particular at examples from preliminary research on a local self-organised urban community and discuss what existing concepts in the ecology......In this paper we introduce the concept of community artifact ecology. We argue that taking a community perspective on the concept of artifact ecologies is relevant in HCI because communities are also dealing with multitudes of artifacts, in ways di↵erent that individuals, organizations...... literature are relevant to consider and how they change with the community perspective....

  8. Gene-Gene-Environment Interactions of Serotonin Transporter, Monoamine Oxidase A and Childhood Maltreatment Predict Aggressive Behavior in Chinese Adolescents

    Science.gov (United States)

    Zhang, Yun; Ming, Qing-sen; Yi, Jin-yao; Wang, Xiang; Chai, Qiao-lian; Yao, Shu-qiao

    2017-01-01

    Gene-environment interactions that moderate aggressive behavior have been identified independently in the serotonin transporter (5-HTT) gene and monoamine oxidase A gene (MAOA). The aim of the present study was to investigate epistasis interactions between MAOA-variable number tandem repeat (VNTR), 5-HTTlinked polymorphism (LPR) and child abuse and the effects of these on aggressive tendencies in a group of otherwise healthy adolescents. A group of 546 Chinese male adolescents completed the Child Trauma Questionnaire and Youth self-report of the Child Behavior Checklist. Buccal cells were collected for DNA analysis. The effects of childhood abuse, MAOA-VNTR, 5-HTTLPR genotypes and their interactive gene-gene-environmental effects on aggressive behavior were analyzed using a linear regression model. The effect of child maltreatment was significant, and a three-way interaction among MAOA-VNTR, 5-HTTLPR and sexual abuse (SA) relating to aggressive behaviors was identified. Chinese male adolescents with high expression of the MAOA-VNTR allele and 5-HTTLPR “SS” genotype exhibited the highest aggression tendencies with an increase in SA during childhood. The findings reported support aggression being a complex behavior involving the synergistic effects of gene-gene-environment interactions. PMID:28203149

  9. A variant in the KCNQ1 gene predicts future type 2 diabetes and mediates impaired insulin secretion

    DEFF Research Database (Denmark)

    Jonsson, Anna Elisabet; Isomaa, Bo; Tuomi, Tiinamaija;

    2009-01-01

    Two independent genome-wide association studies for type 2 diabetes in Japanese subjects have recently identified common variants in the KCNQ1 gene that are strongly associated with type 2 diabetes. Here we studied whether a common variant in KCNQ1 would influence BMI as well as insulin secretion...... and action and predict future type 2 diabetes in subjects from Sweden and Finland....

  10. Interactions between Serotonin Transporter Gene Haplotypes and Quality of Mothers' Parenting Predict the Development of Children's Noncompliance

    Science.gov (United States)

    Sulik, Michael J.; Eisenberg, Nancy; Lemery-Chalfant, Kathryn; Spinrad, Tracy L.; Silva, Kassondra M.; Eggum, Natalie D.; Betkowski, Jennifer A.; Kupfer, Anne; Smith, Cynthia L.; Gaertner, Bridget; Stover, Daryn A.; Verrelli, Brian C.

    2012-01-01

    The LPR and STin2 polymorphisms of the serotonin transporter gene (SLC6A4) were combined into haplotypes that, together with quality of maternal parenting, were used to predict initial levels and linear change in children's (N = 138) noncompliance and aggression from age 18-54 months. Quality of mothers' parenting behavior was observed when…

  11. Arabidopsis CPR5 is a senescence-regulatory gene with pleiotropic functions as predicted by the evolutionary theory of senescence

    NARCIS (Netherlands)

    Jing, Hai-Chun; Anderson, Lisa; Sturre, Marcel J. G.; Hille, Jacques; Dijkwel, Paul P.

    2007-01-01

    Arabidopsis CPR5 is a senescence-regulatory gene with pleiotropic functions as predicted by the evolutionary theory of senescence Hai-Chun Jing1,2, Lisa Anderson3, Marcel J.G. Sturre1, Jacques Hille1 and Paul P. Dijkwel1,* 1Molecular Biology of Plants, Groningen Biomolecular Sciences and Biotechnolo

  12. Arabidopsis CPR5 is a senescence-regulatory gene with pleiotropic functions as predicted by the evolutionary theory of senescence

    NARCIS (Netherlands)

    Jing, Hai-Chun; Anderson, Lisa; Sturre, Marcel J. G.; Hille, Jacques; Dijkwel, Paul P.

    2007-01-01

    Arabidopsis CPR5 is a senescence-regulatory gene with pleiotropic functions as predicted by the evolutionary theory of senescence Hai-Chun Jing1,2, Lisa Anderson3, Marcel J.G. Sturre1, Jacques Hille1 and Paul P. Dijkwel1,* 1Molecular Biology of Plants, Groningen Biomolecular Sciences and

  13. A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects

    DEFF Research Database (Denmark)

    Mutch, D. M.; Pers, Tune Hannes; Temanni, M. R.

    2011-01-01

    AT) gene expression during a low-calorie diet (LCD) could be used to differentiate and predict subjects who experience successful short-term weight maintenance from subjects who experience weight regain. Design: Forty white women followed a dietary protocol consisting of an 8-wk LCD phase followed by a 6...

  14. A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects

    DEFF Research Database (Denmark)

    Mutch, D. M.; Pers, Tune Hannes; Temanni, M. R.

    2011-01-01

    fatty acid metabolism, citric acid cycle, oxidative phosphorylation, and apoptosis were regulated differently by the LCD in WM and WR subjects. Conclusion: This study suggests that LCD-induced changes in insulin secretion and scAT gene expression may have the potential to predict successful short...

  15. 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

    PURPOSE: Clinically useful molecular markers predicting the clinical course of patients diagnosed with non-muscle-invasive bladder cancer are needed to improve treatment outcome. Here, we validated four previously reported gene expression signatures for molecular diagnosis of disease stage and ca...

  16. DNA methylation of the oxytocin receptor gene predicts neural response to ambiguous social stimuli

    Directory of Open Access Journals (Sweden)

    Allison eJack

    2012-10-01

    Full Text Available Oxytocin and its receptor (OXTR play an important role in a variety of social perceptual and affiliative processes. Individual variability in social information processing likely has a strong heritable component, and as such, many investigations have established an association between common genetic variants of OXTR and variability in the social phenotype. However, to date, these investigations have primarily focused only on changes in the sequence of DNA without considering the role of epigenetic factors. DNA methylation is an epigenetic mechanism by which cells control transcription through modification of chromatin structure. DNA methylation of OXTR decreases expression of the gene and high levels of methylation have been associated with autism spectrum disorders. This link between epigenetic variability and social phenotype allows for the possibility that social processes are under epigenetic control. We hypothesized that the level of DNA methylation of OXTR would predict individual variability in social perception. Using the brain’s sensitivity to displays of animacy as a neural endophenotype of social perception, we found significant associations between the degree of OXTR methylation and brain activity evoked by the perception of animacy. Our results suggest that consideration of DNA methylation may substantially improve our ability to explain individual differences in imaging genetic association studies.

  17. Predicting human miRNA target genes using a novel evolutionary methodology

    KAUST Repository

    Aigli, Korfiati

    2012-01-01

    The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3\\'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset. © 2012 Springer-Verlag.

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

    Science.gov (United States)

    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.

  19. Artifact Diamond Its Allure And Significance

    Science.gov (United States)

    Yoder, Max N.

    1989-01-01

    While the preponderance of the mechanical, optical, and electronic properties of natural diamond have been known for over a decade, only recently has artifact diamond in technologically useful form factors become an exciting possibility. The advent of sacrificial, lattice matched crystalline substrates provides the basis not only for semiconducting applications of diamond, but for optical mirrors, lenses, and windows as well. As a semiconductor, diamond has the highest resistivity, the highest saturated electron velocity, the highest thermal conductivity, the lowest dielectric constant, the highest dielectric strength, the greatest hardness, the largest bandgap and the smallest lattice constant of any material. It also has electron and hole mobilities greater than those of silicon. Its figure of merit as a microwave power amplifier is unexcelled and exceeds that of silicon by a multiplier of 8200. For integrated circuit potential, its thermal conductivity, saturated velocity, and dielectric constant also place it in the premier position (32 times that of silicon, 46 times that of GaAs). Although not verified, its radiation hardness should also be unmatched. Aside from its brilliant sparkle as a gemstone, there has been little use of diamond in the field of optics. Processing of the diamond surface now appears to be as simple as that of any other material --albeit with different techniques. In fact, it may be possible to etch diamond far more controllably (at economically viable rates) than any other material as the product of the etch is gaseous and the etched trough is self-cleaning. Other properties of diamond make it an ideal optical material. Among them are its unmatched thermal conductivity, its extremely low absorption loss above 228 nanometers, and unmatched Young's modulus, Poisson's ratio, tensile strength, hardness, thermal shock, and modulus of elasticity. If the recently-found mechanisms by which erbium impurities in III-V junctions can be made to "lase

  20. Analytic solutions of an unclassified artifact /

    Energy Technology Data Exchange (ETDEWEB)

    Trent, Bruce C.

    2012-03-01

    This report provides the technical detail for analytic solutions for the inner and outer profiles of the unclassified CMM Test Artifact (LANL Part Number 157Y-700373, 5/03/2001) in terms of radius and polar angle. Furthermore, analytic solutions are derived for the legacy Sheffield measurement hardware, also in terms of radius and polar angle, using part coordinates, i.e., relative to the analytic profile solutions obtained. The purpose of this work is to determine the exact solution for the “cosine correction” term inherent to measurement with the Sheffield hardware. The cosine correction is required in order to interpret the actual measurements taken by the hardware in terms of an actual part definition, or “knot-point spline definition,” that typically accompanies a component drawing. Specifically, there are two portions of the problem: first an analytic solution must be obtained for any point on the part, e.g., given the radii and the straight lines that define the part, it is required to find an exact solution for the inner and outer profile for any arbitrary polar angle. Next, the problem of the inspection of this part must be solved, i.e., given an arbitrary sphere (representing the inspection hardware) that comes in contact with the part (inner and outer profiles) at any arbitrary polar angle, it is required to determine the exact location of that intersection. This is trivial for the case of concentric circles. In the present case, however, the spherical portion of the profiles is offset from the defined center of the part, making the analysis nontrivial. Here, a simultaneous solution of the part profiles and the sphere was obtained.

  1. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

    Directory of Open Access Journals (Sweden)

    Xu Huilei

    2010-12-01

    Full Text Available Abstract Background Mouse embryonic stem cells (mESCs are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership. Results For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG using support vector machines (SVM. The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier. Conclusions Our results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high

  2. PRGPred: A platform for prediction of domains of resistance gene analogue (RGA in Arecaceae developed using machine learning algorithms

    Directory of Open Access Journals (Sweden)

    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.

  3. Predicting the Pathogenic Potential of BRCA1 and BRCA2 Gene Variants Identified in Clinical Genetic Testing

    Directory of Open Access Journals (Sweden)

    Clare Brookes

    2015-05-01

    Full Text Available Objectives: Missense variants are very commonly detected when screening for mutations in the BRCA1 and BRCA2 genes. Pathogenic mutations in the BRCA1 and BRCA2 genes lead to an increased risk of developing breast, ovarian, prostate and/or pancreatic cancer. This study aimed to assess the predictive capability of in silico programmes and mutation databases in assisting diagnostic laboratories to determine the pathogenicity of sequence-detectable mutations. Methods: Between July 2011 and April 2013, an analysis was undertaken of 13 missense BRCA gene variants that had been detected in patients referred to the Genetic Health Services New Zealand (Northern Hub for BRCA gene analysis. The analysis involved the use of 13 in silico protein prediction programmes, two in silico transcript analysis programmes and the examination of three BRCA gene databases. Results: In most of the variants, the analysis showed different in silico interpretations. This illustrates the interpretation challenges faced by diagnostic laboratories. Conclusion: Unfortunately, when using online mutation databases and carrying out in silico analyses, there is significant discordance in the classification of some missense variants in the BRCA genes. This discordance leads to complexities in interpreting and reporting these variants in a clinical context. The authors have developed a simple procedure for analysing variants; however, those of unknown significance largely remain unknown. As a consequence, the clinical value of some reports may be negligible.

  4. Local gene density predicts the spatial position of genetic loci in the interphase nucleus.

    Science.gov (United States)

    Murmann, Andrea E; Gao, Juntao; Encinosa, Marissa; Gautier, Mathieu; Peter, Marcus E; Eils, Roland; Lichter, Peter; Rowley, Janet D

    2005-11-15

    Specific chromosomal translocations are hallmarks of many human leukemias. The basis for these translocation events is poorly understood, but it has been assumed that spatial positioning of genes in the nucleus of hematopoietic cells is a contributing factor. Analysis of the nuclear 3D position of the gene MLL, frequently involved in chromosomal translocations and five of its translocation partners (AF4, AF6, AF9, ENL and ELL), and two control loci revealed a characteristic radial distribution pattern in all hematopoietic cells studied. Genes in areas of high local gene density were found positioned towards the nuclear center, whereas genes in regions of low gene density were detected closer to the nuclear periphery. The gene density within a 2 Mbp window was found to be a better predictor for the relative positioning of a genomic locus within the cell nucleus than the gene density of entire chromosomes. Analysis of the position of MLL, AF4, AF6 and AF9 in cell lines carrying chromosomal translocations involving these genes revealed that the position of the normal genes was different from that of the fusion genes, and this was again consistent with the changes in local gene density within a 2 Mbp window. Thus, alterations in gene density directly at translocation junctions could explain the change in the position of affected genes in leukemia cells.

  5. Search for continuous gravitational waves: improving robustness versus instrumental artifacts

    CERN Document Server

    Keitel, David; Papa, Maria Alessandra; Leaci, Paola; Siddiqi, Maham

    2013-01-01

    The standard multi-detector F-statistic for continuous gravitational waves is susceptible to false alarms from instrumental artifacts, for example monochromatic sinusoidal disturbances (lines). This vulnerability to line artifacts arises because the F-statistic compares the signal hypothesis to a Gaussian-noise hypothesis, and hence is triggered by anything that resembles the signal hypothesis more than Gaussian noise. Various ad-hoc veto methods to deal with such line artifacts have been proposed and used in the past. Here we develop a Bayesian framework that includes an explicit alternative hypothesis to model disturbed data. We introduce a simple line model that defines lines as signal candidates appearing only in one detector. This allows us to explicitly compute the odds between the signal hypothesis and an extended noise hypothesis, resulting in a new detection statistic that is more robust to instrumental artifacts. We present and discuss results from Monte-Carlo tests on both simulated data and on det...

  6. How Do Artifact Models Help Direct SPI Projects?

    DEFF Research Database (Denmark)

    Kuhrmann, Marco; Richardson, Ita

    2015-01-01

    To overcome shortcomings associated with software process improvement (SPI), we previously recommended that process engineers focus on the artifacts to be developed in SPI projects. These artifacts should define desired outcomes, rather than specific methods. During this prior research, we...... developed a model for Artifact-based Software Process Improvement & Management (ArSPI). We are now carrying out studies to confirm our claims that ArSPI will provide benefits such as quality assurance. In this paper, we report on an experimental setting in which we developed and analyzed a strategy to use...... artifact models to direct process model improvement. We analyzed a process specification, the realized model, and the generated electronic process guide. We used ArSPI v0.9 as our process model and the Capability Maturity Model Integration (CMMI) as an external reference to provide a set of overall...

  7. Cultural Artifact Detection in Long Wave Infrared Imagery.

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Dylan Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Craven, Julia M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ramon, Eric [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-01-01

    Detection of cultural artifacts from airborne remotely sensed data is an important task in the context of on-site inspections. Airborne artifact detection can reduce the size of the search area the ground based inspection team must visit, thereby improving the efficiency of the inspection process. This report details two algorithms for detection of cultural artifacts in aerial long wave infrared imagery. The first algorithm creates an explicit model for cultural artifacts, and finds data that fits the model. The second algorithm creates a model of the background and finds data that does not fit the model. Both algorithms are applied to orthomosaic imagery generated as part of the MSFE13 data collection campaign under the spectral technology evaluation project.

  8. Information Content Across Types of Nurse Cognitive Artifacts.

    Science.gov (United States)

    Blaz, Jacquelyn W; Doig, Alexa K; Cloyes, Kristin G; Staggers, Nancy

    2016-01-01

    Acute care nurses commonly use personalized cognitive artifacts to organize information during a shift. The purpose of this content analysis is to compare information content across three formats of cognitive artifacts used by acute care nurses in a medical oncology unit: hand-made free-form, preprinted skeleton, and EHR-generated. Information contained in free-form and skeleton artifacts is more tailored to specific patient context than the NSR. Free-form and skeleton artifacts provide a space for synthesizing information to construct a "story of the patient" that is missing in the NSR. Future design of standardized handoff tools will need to take these differences into account for successful adoption by acute care nurses, including tailoring of information by patient, not just unit type, and allowing a space for nurses to construct a narrative describing the patients "story."

  9. THE DISABLED AND ART: SELECTED ARTIFACTS OF GHANAIAN ...

    African Journals Online (AJOL)

    made into the artifacts of six (6) selected disabled artists in Ashanti Region. Description of ten ... 2010 Kwame Nkrumah University of Science and Technology (KNUST). Journal of Science and .... pation and women empowerment. There is no.

  10. Automatic correction of dental artifacts in PET/MRI

    DEFF Research Database (Denmark)

    Ladefoged, Claes N.; Andersen, Flemming L.; Keller, Sune;

    2015-01-01

    A challenge when using current magnetic resonance (MR)-based attenuation correction in positron emission tomography/MR imaging (PET/MRI) is that the MRIs can have a signal void around the dental fillings that is segmented as artificial air-regions in the attenuation map. For artifacts connected...... to the background, we propose an extension to an existing active contour algorithm to delineate the outer contour using the non-attenuation corrected PET image and the original attenuation map. We propose a combination of two different methods for differentiating the artifacts within the body from the anatomical...... air-regions by first using a template of artifact regions, and second, representing the artifact regions with a combination of active shape models and k-nearest-neighbors. The accuracy of the combined method has been evaluated using 25 18 F-fluorodeoxyglucose PET/MR patients. Results showed...

  11. Automatic correction of dental artifacts in PET/MRI

    DEFF Research Database (Denmark)

    Ladefoged, Claes N.; Andersen, Flemming L.; Keller, Sune

    2015-01-01

    A challenge when using current magnetic resonance (MR)-based attenuation correction in positron emission tomography/MR imaging (PET/MRI) is that the MRIs can have a signal void around the dental fillings that is segmented as artificial air-regions in the attenuation map. For artifacts connected...... to the background, we propose an extension to an existing active contour algorithm to delineate the outer contour using the non-attenuation corrected PET image and the original attenuation map. We propose a combination of two different methods for differentiating the artifacts within the body from the anatomical...... air-regions by first using a template of artifact regions, and second, representing the artifact regions with a combination of active shape models and k-nearest-neighbors. The accuracy of the combined method has been evaluated using 25 18 F-fluorodeoxyglucose PET/MR patients. Results showed...

  12. Metal artifact reduction based on the combined prior image

    CERN Document Server

    Zhang, Yanbo

    2014-01-01

    Metallic implants introduce severe artifacts in CT images, which degrades the image quality. It is an effective method to reduce metal artifacts by replacing the metal affected projection with the forward projection of a prior image. How to find a good prior image is the key of this class methods, and numerous algorithms have been proposed to address this issue recently. In this work, by using image mutual correlation, pixels in the original reconstructed image or linear interpolation corrected image, which are less affected by artifacts, are selected to build a combined image. Thereafter, a better prior image is generated from the combined image by using tissue classification. The results of three patients' CT images show that the proposed method can reduce metal artifacts remarkably.

  13. A new interpretation of distortion artifacts in sweep measurements

    DEFF Research Database (Denmark)

    Torras Rosell, Antoni; Jacobsen, Finn

    2011-01-01

    The characterization of acoustical spaces by means of impulse response measurements is often biased by the nonlinear behavior of the loudspeaker used to excite the system under test. In this context the distortion immunity provided by the sweep technique has been investigated. The results show th...... that the sweep method can reject a significant amount of distortion artifacts but, in contrast to what is claimed in the literature, it cannot exclude all distortion artifacts from the causal part of the estimated impulse response....

  14. Mitigation of artifacts in rtm with migration kernel decomposition

    KAUST Repository

    Zhan, Ge

    2012-01-01

    The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently. In this paper, we present a generalized diffraction-stack migration approach for reducing RTM artifacts via decomposition of migration kernel. The decomposition leads to an improved understanding of migration artifacts and, therefore, presents us with opportunities for improving the quality of RTM images.

  15. Reduction of metal artifacts: beam hardening and photon starvation effects

    Science.gov (United States)

    Yadava, Girijesh K.; Pal, Debashish; Hsieh, Jiang

    2014-03-01

    The presence of metal-artifacts in CT imaging can obscure relevant anatomy and interfere with disease diagnosis. The cause and occurrence of metal-artifacts are primarily due to beam hardening, scatter, partial volume and photon starvation; however, the contribution to the artifacts from each of them depends on the type of hardware. A comparison of CT images obtained with different metallic hardware in various applications, along with acquisition and reconstruction parameters, helps understand methods for reducing or overcoming such artifacts. In this work, a metal beam hardening correction (BHC) and a projection-completion based metal artifact reduction (MAR) algorithms were developed, and applied on phantom and clinical CT scans with various metallic implants. Stainless-steel and Titanium were used to model and correct for metal beam hardening effect. In the MAR algorithm, the corrupted projection samples are replaced by the combination of original projections and in-painted data obtained by forward projecting a prior image. The data included spine fixation screws, hip-implants, dental-filling, and body extremity fixations, covering range of clinically used metal implants. Comparison of BHC and MAR on different metallic implants was used to characterize dominant source of the artifacts, and conceivable methods to overcome those. Results of the study indicate that beam hardening could be a dominant source of artifact in many spine and extremity fixations, whereas dental and hip implants could be dominant source of photon starvation. The BHC algorithm could significantly improve image quality in CT scans with metallic screws, whereas MAR algorithm could alleviate artifacts in hip-implants and dentalfillings.

  16. Situating Creative Artifacts in Art and Design Research

    Directory of Open Access Journals (Sweden)

    Nithikul Nimkulrat

    2013-09-01

    Full Text Available This article aims to discuss the position of art and design artifacts, and their creation, in a practice-led research process.  Two creative productions and exhibitions featuring my textile artifacts were intentionally carried out in order to tackle a specific research problem, and these will be examined here as case studies.  These cases cover the production and exhibition of two sets of artworks, named Seeing Paper and Paper World, that were created as part of my completed doctoral research entitled Paperness: Expressive Material inTextile Art from an Artist’s Viewpoint. The study examined the relationship between a physical material and artistic expression in textile art and design.  Both cases exemplify the roles of creative productions and artifacts situated in the process of inquiry.  Throughout a practice-led research process, art and design artifacts can serve as inputs into knowledge production and as outputs for knowledge communication.  As inputs, both art productions and artifacts can be the starting point of a research project from which the research question is formulated.  They can also provide data for analysis from which knowledge is constructed.  Asoutputs, artifacts can indicate whether the research problem requires reformulation, demonstrate the experiential knowledge of the creative process, and strengthen the findings articulated in the written output.  Creative practice in a research context can contribute to generating or enhancing the knowledge which is embedded in the practice and embodied by the practitioner.  This knowledge or insight can be obtained from the artist creating the artifact, the artifact created, the process of making it, and the culture in which it is produced and viewed or used, all taking place at different stages of a research process.

  17. Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT)

    Science.gov (United States)

    Saiag, P.; Gutzmer, R.; Ascierto, P. A.; Maio, M.; Grob, J.-J.; Murawa, P.; Dreno, B.; Ross, M.; Weber, J.; Hauschild, A.; Rutkowski, P.; Testori, A.; Levchenko, E.; Enk, A.; Misery, L.; Vanden Abeele, C.; Vojtek, I.; Peeters, O.; Brichard, V. G.; Therasse, P.

    2016-01-01

    Background Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. Patients and methods An open-label prospective phase II trial (‘PREDICT’) in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Results Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS− populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS− patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS−). There was one complete response (GS−) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Conclusion Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS− and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study. This study is registered at www.clinicatrials.gov NCT00942162. PMID:27502712

  18. A hybrid intelligence approach to artifact recognition in digital publishing

    Science.gov (United States)

    Vega-Riveros, J. Fernando; Santos Villalobos, Hector J.

    2006-02-01

    The system presented integrates rule-based and case-based reasoning for artifact recognition in Digital Publishing. In Variable Data Printing (VDP) human proofing could result prohibitive since a job could contain millions of different instances that may contain two types of artifacts: 1) evident defects, like a text overflow or overlapping 2) style-dependent artifacts, subtle defects that show as inconsistencies with regard to the original job design. We designed a Knowledge-Based Artifact Recognition tool for document segmentation, layout understanding, artifact detection, and document design quality assessment. Document evaluation is constrained by reference to one instance of the VDP job proofed by a human expert against the remaining instances. Fundamental rules of document design are used in the rule-based component for document segmentation and layout understanding. Ambiguities in the design principles not covered by the rule-based system are analyzed by case-based reasoning, using the Nearest Neighbor Algorithm, where features from previous jobs are used to detect artifacts and inconsistencies within the document layout. We used a subset of XSL-FO and assembled a set of 44 document samples. The system detected all the job layout changes, while obtaining an overall average accuracy of 84.56%, with the highest accuracy of 92.82%, for overlapping and the lowest, 66.7%, for the lack-of-white-space.

  19. Artifact removal in physiological signals--practices and possibilities.

    Science.gov (United States)

    Sweeney, Kevin T; Ward, Tomás E; McLoone, Seán F

    2012-05-01

    The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

  20. Artifact reduction in HARP strain maps using anisotropic smoothing

    Science.gov (United States)

    Abd-Elmoniem, Khaled Z.; Parthasarathy, Vijay; Prince, Jerry L.

    2006-03-01

    Harmonic phase (HARP) MRI is used to measure myocardial motion and strain from tagged MR images. HARP MRI uses limited number of samples from the spectrum of the tagged images to reconstruct motion and strain. The HARP strain maps, however, suffer from artifacts that limit the accuracy of the computations and degrade the appearance of the strain maps. Causes of these, so called 'zebra', artifacts include image noise, Gibbs ringing, and interference from other Fourier spectral peaks. Computing derivatives of the HARP phase, which are needed to estimate strain, further accentuates these artifacts. Previous methods to reduce these artifacts include 1-D and 2-D nonlinear filtering of the HARP derivatives, and a 2-D linear filtering of unwrapped HARP phase. A common drawback among these methods is the lack of proper segmentation of the myocardium from the blood pool. Because of the lack of segmentation, the noisy phase values from the blood pool enter into the computation in the smoothed strain maps, which causes artifacts. In this work, we propose a smoothing method based on anisotropic diffusion that filters the HARP derivatives strictly within the myocardium without the need for prior segmentation. The information about tissue geometry and the strain distribution is used to restrict the smoothing to within the myocardium, thereby ensuring minimum distortion of the final strain map. Preliminary results demonstrate the ability of anisotropic diffusion for better artifact reduction and lesser strain distortion than the existing methods.

  1. Artifact-Based Transformation of IBM Global Financing

    Science.gov (United States)

    Chao, Tian; Cohn, David; Flatgard, Adrian; Hahn, Sandy; Linehan, Mark; Nandi, Prabir; Nigam, Anil; Pinel, Florian; Vergo, John; Wu, Frederick Y.

    IBM Global Financing (IGF) is transforming its business using the Business Artifact Method, an innovative business process modeling technique that identifies key business artifacts and traces their life cycles as they are processed by the business. IGF is a complex, global business operation with many business design challenges. The Business Artifact Method is a fundamental shift in how to conceptualize, design and implement business operations. The Business Artifact Method was extended to solve the problem of designing a global standard for a complex, end-to-end process while supporting local geographic variations. Prior to employing the Business Artifact method, process decomposition, Lean and Six Sigma methods were each employed on different parts of the financing operation. Although they provided critical input to the final operational model, they proved insufficient for designing a complete, integrated, standard operation. The artifact method resulted in a business operations model that was at the right level of granularity for the problem at hand. A fully functional rapid prototype was created early in the engagement, which facilitated an improved understanding of the redesigned operations model. The resulting business operations model is being used as the basis for all aspects of business transformation in IBM Global Financing.

  2. Inference and coherence in causal-based artifact categorization.

    Science.gov (United States)

    Puebla, Guillermo; Chaigneau, Sergio E

    2014-01-01

    In four experiments, we tested conditions under which artifact concepts support inference and coherence in causal categorization. In all four experiments, participants categorized scenarios in which we systematically varied information about artifacts' associated design history, physical structure, user intention, user action and functional outcome, and where each property could be specified as intact, compromised or not observed. Consistently across experiments, when participants received complete information (i.e., when all properties were observed), they categorized based on individual properties and did not show evidence of using coherence to categorize. In contrast, when the state of some property was not observed, participants gave evidence of using available information to infer the state of the unobserved property, which increased the value of the available information for categorization. Our data offers answers to longstanding questions regarding artifact categorization, such as whether there are underlying causal models for artifacts, which properties are part of them, whether design history is an artifact's causal essence, and whether physical appearance or functional outcome is the most central artifact property.

  3. Detection of eye blink artifacts from single prefrontal channel electroencephalogram.

    Science.gov (United States)

    Chang, Won-Du; Cha, Ho-Seung; Kim, Kiwoong; Im, Chang-Hwan

    2016-02-01

    Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain-computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.

  4. Psychrophilic proteases dramatically reduce single cell RNA-seq artifacts: A molecular atlas of kidney development.

    Science.gov (United States)

    Adam, Mike; Potter, Andrew S; Potter, S Steven

    2017-08-29

    Single cell RNA-seq is a powerful methodology. Nevertheless there are important limitations, including the technical challenges of breaking down an organ or tissue into a single cell suspension. Invariably this has required enzymatic incubation at 37°C, which can be expected to result in artifact changes in gene expression patterns. We here describe a dissociation method that uses a protease with high activity in the cold, purified from a psychrophilic microorganism. The entire procedure is carried out at 6°C or colder, where mammalian transcriptional machinery is largely inactive, thereby effectively "freezing in" the in vivo gene expression patterns. To test this method we carried out RNA-seq on 20,424 single cells from P1 mouse kidneys, comparing the results of the psychrophilic protease method with procedures using 37°C incubation. We show that the cold protease method provides a great reduction in gene expression artifacts. In addition the results produce a single cell resolution gene expression atlas of the newborn mouse kidney, an interesting time in development when mature nephrons are present yet nephrogenesis remains extremely active. © 2017. Published by The Company of Biologists Ltd.

  5. Unraveling toxicological mechanisms and predicting toxicity classes with gene dysregulation networks

    NARCIS (Netherlands)

    Pronk, T.E.; Someren, P. van; Stierum, R.H.; Ezendam, J.; Pennings, J.L.A.

    2013-01-01

    The use of genes for distinguishing classes of toxicity has become well established. In this paper we combine the reconstruction of a gene dysregulation network (GDN) with a classifier to assign unseen compounds to their appropriate class. Gene pairs in the GDN are dysregulated in the sense that the

  6. Unraveling toxicological mechanisms and predicting toxicity classes with gene dysregulation networks

    NARCIS (Netherlands)

    Pronk, T.E.; Someren, P. van; Stierum, R.H.; Ezendam, J.; Pennings, J.L.A.

    2013-01-01

    The use of genes for distinguishing classes of toxicity has become well established. In this paper we combine the reconstruction of a gene dysregulation network (GDN) with a classifier to assign unseen compounds to their appropriate class. Gene pairs in the GDN are dysregulated in the sense that

  7. Prediction of lung cancer based on serum biomarkers by gene expression programming methods.

    Science.gov (United States)

    Yu, Zhuang; Chen, Xiao-Zheng; Cui, Lian-Hua; Si, Hong-Zong; Lu, Hai-Jiao; Liu, Shi-Hai

    2014-01-01

    In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are frequently- used lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

  8. Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes.

    Science.gov (United States)

    Pujato, Mario; Kieken, Fabien; Skiles, Amanda A; Tapinos, Nikos; Fiser, Andras

    2014-12-16

    Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  9. Fine mapping and candidate gene prediction of the photoperiod and thermo-sensitive genic male sterile gene pms1(t) in rice

    Institute of Scientific and Technical Information of China (English)

    Yuan-fei ZHOU; Xian-yin ZHANG; Qing-zhong XUE

    2011-01-01

    Pei'ai64S, an indica sterile variety with photoperiod and thermo-sensitive genic male sterile (PTGMS) genes, has been widely exploited for commercial seed production for "two-line" hybrid rice in China. One PTGMS gene from Pei'ai64S, pms1(t), was mapped by a strategy of bulked-extreme and recessive-class approach with simple sequence repeat (SSR) and insert and deletion (In-Del) markers. Using linkage analysis for the F2 mapping population consisting of 320 completely male sterile individuals derived from a cross between Pei'ai64S and 93-11 (indica restorer) lines, the pms1(t) gene was delimited to the region between the RM21242 (0.2 cM) and YF11 (0.2 cM) markers on the short arm of chromosome 7. The interval containing the pms1(t) locus, which was co-segregated with RM6776, is a 101.1 kb region based on the Nipponbare rice genome. Fourteen predicted loci were found in this region by the Institute for Genomic Research (TIGR) Genomic Annotation. Based on the function of the locus LOC_Os07g12130 by bioinformatics analysis, it is predicted to encode a protein containing a Myb-like DNA-binding domain, and may process the transcript with thermosensory response. The reverse transcription-polymerase chain reaction (RT-PCR) results revealed that the mRNA levels of LOC_Os07g12130 were altered in different photoperiod and temperature treatments. Thus, the LOC_Os07g12130 locus is the most likely candidate gene for pms1(t). These results may facilitate not only using the molecular marker assisted selection of PTGMS genes, but also cloning of the pms1(t) gene itself.

  10. Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy.

    Science.gov (United States)

    Huang, Chi-Cheng; Tu, Shih-Hsin; Huang, Ching-Shui; Lien, Heng-Hui; Lai, Liang-Chuan; Chuang, Eric Y

    2013-01-01

    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.

  11. A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

    Directory of Open Access Journals (Sweden)

    Stigler Brandilyn

    2012-06-01

    Full Text Available Abstract Background Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. Results To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle and ectodermal (skin cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes

  12. A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development.

    Science.gov (United States)

    Stigler, Brandilyn; Chamberlin, Helen M

    2012-06-26

    Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9. This work provides a strong

  13. Metal-related artifacts in instrumented spine. Techniques for reducing artifacts in CT and MRI: state of the art

    OpenAIRE

    Stradiotti, P.; Curti, A.; G. Castellazzi; Zerbi, A.

    2009-01-01

    The projectional nature of radiogram limits its amount of information about the instrumented spine. MRI and CT imaging can be more helpful, using cross-sectional view. However, the presence of metal-related artifacts at both conventional CT and MRI imaging can obscure relevant anatomy and disease. We reviewed the literature about overcoming artifacts from metallic orthopaedic implants at high-field strength MRI imaging and multi-detector CT. The evolution of multichannel CT has made available...

  14. Hierarchy of gene expression data is predictive of future breast cancer outcome

    Science.gov (United States)

    Chen, Man; Deem, Michael W.

    2013-10-01

    We calculate measures of hierarchy in gene and tissue networks of breast cancer patients. We find that the likelihood of metastasis in the future is correlated with increased values of network hierarchy for expression networks of cancer-associated genes, due to the correlated expression of cancer-specific pathways. Conversely, future metastasis and quick relapse times are negatively correlated with the values of network hierarchy in the expression network of all genes, due to the dedifferentiation of gene pathways and circuits. These results suggest that the hierarchy of gene expression may be useful as an additional biomarker for breast cancer prognosis.

  15. Seven-CpG-based prognostic signature coupled with gene expression predicts survival of oral squamous cell carcinoma.

    Science.gov (United States)

    Shen, Sipeng; Wang, Guanrong; Shi, Qianwen; Zhang, Ruyang; Zhao, Yang; Wei, Yongyue; Chen, Feng; Christiani, David C

    2017-01-01

    DNA methylation has started a recent revolution in genomics biology by identifying key biomarkers for multiple cancers, including oral squamous cell carcinoma (OSCC), the most common head and neck squamous cell carcinoma. A multi-stage screening strategy was used to identify DNA-methylation-based signatures for OSCC prognosis. We used The Cancer Genome Atlas (TCGA) data as training set which were validated in two independent datasets from Gene Expression Omnibus (GEO). The correlation between DNA methylation and corresponding gene expression and the prognostic value of the gene expression were explored as well. The seven DNA methylation CpG sites were identified which were significantly associated with OSCC overall survival. Prognostic signature, a weighted linear combination of the seven CpG sites, successfully distinguished the overall survival of OSCC patients and had a moderate predictive ability for survival [training set: hazard ratio (HR) = 3.23, P = 5.52 × 10(-10), area under the curve (AUC) = 0.76; validation set 1: HR = 2.79, P = 0.010, AUC = 0.67; validation set 2: HR = 3.69, P = 0.011, AUC = 0.66]. Stratification analysis by human papillomavirus status, clinical stage, age, gender, smoking status, and grade retained statistical significance. Expression of genes corresponding to candidate CpG sites (AJAP1, SHANK2, FOXA2, MT1A, ZNF570, HOXC4, and HOXB4) was also significantly associated with patient's survival. Signature integrating of DNA methylation, gene expression, and clinical information showed a superior ability for prognostic prediction (AUC = 0.78). Prognostic signature integrated of DNA methylation, gene expression, and clinical information provides a better prognostic prediction value for OSCC patients than that with clinical information only.

  16. Arabidopsis CPR5 is a senescence-regulatory gene with pleiotropic functions as predicted by the evolutionary theory of senescence.

    Science.gov (United States)

    Jing, Hai-Chun; Anderson, Lisa; Sturre, Marcel J G; Hille, Jacques; Dijkwel, Paul P

    2007-01-01

    Evolutionary theories of senescence predict that genes with pleiotropic functions are important for senescence regulation. In plants there is no direct molecular genetic test for the existence of such senescence-regulatory genes. Arabidopsis cpr5 mutants exhibit multiple phenotypes including hypersensitivity to various signalling molecules, constitutive expression of pathogen-related genes, abnormal trichome development, spontaneous lesion formation, and accelerated leaf senescence. These indicate that CPR5 is a beneficial gene which controls multiple facets of the Arabidopsis life cycle. Ectopic expression of CPR5 restored all the mutant phenotypes. However, in transgenic plants with increased CPR5 transcripts, accelerated leaf senescence was observed in detached leaves and at late development around 50 d after germination, as illustrated by the earlier onset of senescence-associated physiological and molecular markers. Thus, CPR5 has early-life beneficial effects by repressing cell death and insuring normal plant development, but late-life deleterious effects by promoting developmental senescence. As such, CPR5 appears to function as a typical senescence-regulatory gene as predicted by the evolutionary theories of senescence.

  17. Gene-gene interactions between HNF4A and KCNJ11 in predicting Type 2 diabetes in women

    NARCIS (Netherlands)

    Qi, L.; van Dam, R. M.; Asselbergs, F. W.; Hu, F. B.

    2007-01-01

    Aims Recent studies indicate transcription factor hepatocyte nuclear factor 4 alpha ( HNF-4 alpha, HNF4A) modulates the transcription of the pancreatic B-cell ATP-sensitive K+ ( K-ATP) channel subunit Kir6.2 gene ( KCNJ11). Both HNF4A and KCNJ11 have previously been associated with diabetes risk but

  18. Metal-related artifacts in instrumented spine. Techniques for reducing artifacts in CT and MRI: state of the art.

    Science.gov (United States)

    Stradiotti, P; Curti, A; Castellazzi, G; Zerbi, A

    2009-06-01

    The projectional nature of radiogram limits its amount of information about the instrumented spine. MRI and CT imaging can be more helpful, using cross-sectional view. However, the presence of metal-related artifacts at both conventional CT and MRI imaging can obscure relevant anatomy and disease. We reviewed the literature about overcoming artifacts from metallic orthopaedic implants at high-field strength MRI imaging and multi-detector CT. The evolution of multichannel CT has made available new techniques that can help minimizing the severe beam-hardening artifacts. The presence of artifacts at CT from metal hardware is related to image reconstruction algorithm (filter), tube current (in mA), X-ray kilovolt peak, pitch, hardware composition, geometry (shape), and location. MRI imaging has been used safely in patients with orthopaedic metallic implants because most of these implants do not have ferromagnetic properties and have been fixed into position. However, on MRI imaging metallic implants may produce geometric distortion, the so-called susceptibility artifact. In conclusion, although 140 kV and high milliamperage second exposures are recommended for imaging patients with hardware, caution should always be exercised, particularly in children, young adults, and patients undergoing multiple examinations. MRI artifacts can be minimized by positioning optimally and correctly the examined anatomy part with metallic implants in the magnet and by choosing fast spin-echo sequences, and in some cases also STIR sequences, with an anterior to posterior frequency-encoding direction and the smallest voxel size.

  19. Aberrant gene methylation in the peritoneal fluid is a risk factor predicting peritoneal recurrence in gastric cancer

    Institute of Scientific and Technical Information of China (English)

    Masatsugu; Hiraki; Yoshihiko; Kitajima; Seiji; Sato; Jun; Nakamura; Kazuyoshi; Hashiguchi; Hirokazu; Noshiro; Kohji; Miyazaki

    2010-01-01

    AIM:To investigate whether gene methylation in the peritoneal fluid (PF) predicts peritoneal recurrence in gastric cancer patients.METHODS: The gene methylation of CHFR (checkpoint with forkhead and ring finger domains), p16, RUNX3 (runt-related transcription factor 3), E-cadherin, hMLH1 (mutL homolog 1), ABCG2 (ATP-binding cassette, sub-family G, member 2) and BNIP3 (BCL2/adenovirus E1B 19 kDa interacting protein 3) were analyzed in 80 specimens of PF by quantitative methylation-specific polymerase chain r...

  20. Next-generation sequencing of the porcine skeletal muscle transcriptome for computational prediction of microRNA gene targets.

    Directory of Open Access Journals (Sweden)

    Tara G McDaneld

    Full Text Available BACKGROUND: MicroRNA are a class of small RNAs that regulate gene expression by inhibiting translation of protein encoding transcripts through targeting of a microRNA-protein complex by base-pairing of the microRNA sequence to cognate recognition sequences in the 3' untranslated region (UTR of the mRNA. Target identification for a given microRNA sequence is generally accomplished by informatics analysis of predicted mRNA sequences present in the genome or in databases of transcript sequence for the tissue of interest. However, gene models for porcine skeletal muscle transcripts in current databases, specifically complete sequence of the 3' UTR, are inadequate for this exercise. METHODOLOGY/PRINCIPAL FINDINGS: To provide data necessary to identify gene targets for microRNA in porcine skeletal muscle, normalized cDNA libraries were sequenced using Roche 454 GS-FLX pyrosequencing and de novo assembly of transcripts enriched in the 3' UTR was performed using the MIRA sequence assembly program. Over 725 million bases of sequence were generated, which assembled into 18,202 contigs. Sequence reads were mapped to a 3' UTR database containing porcine sequences. The 3' UTR that mapped to the database were examined to predict targets for previously identified microRNA that had been separately sequenced from the same porcine muscle sample used to generate the cDNA libraries. For genes with microRNA-targeted 3' UTR, KEGG pathways were computationally determined in order to identify potential functional effects of these microRNA-targeted transcripts. CONCLUSIONS: Through next-generation sequencing of transcripts expressed in skeletal muscle, mapping reads to a 3' UTR database, and prediction of microRNA target sites in the 3' UTR, our results identified genes expressed in porcine skeletal muscle and predicted the microRNA that target these genes. Additionally, identification of pathways regulated by these microRNA-targeted genes provides us with a set of

  1. Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models.

    Science.gov (United States)

    Hu, Hong; Roqueiro, Damian; Dai, Yang

    2011-01-01

    Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize transcription factors for regulatory functional studies. A novel approach based on the use of Lasso regression models is proposed to address this problem. We examine the ability of the Lasso model using time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal wounds in mouse over all stages of wound healing.

  2. Prediction

    CERN Document Server

    Sornette, Didier

    2010-01-01

    This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...

  3. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

    Science.gov (United States)

    Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C.

    2017-01-01

    Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a ‘big data compacting and data fusion’—concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a ‘predictive toxicogenomics space’ (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, lea