WorldWideScience

Sample records for rna-rna interaction prediction

  1. Fast prediction of RNA-RNA interaction using heuristic algorithm.

    Science.gov (United States)

    Montaseri, Soheila

    2015-01-01

    Interaction between two RNA molecules plays a crucial role in many medical and biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. Some algorithms have been formed to predict the structure of the RNA-RNA interaction. High computational time is a common challenge in most of the presented algorithms. In this context, a heuristic method is introduced to accurately predict the interaction between two RNAs based on minimum free energy (MFE). This algorithm uses a few dot matrices for finding the secondary structure of each RNA and binding sites between two RNAs. Furthermore, a parallel version of this method is presented. We describe the algorithm's concurrency and parallelism for a multicore chip. The proposed algorithm has been performed on some datasets including CopA-CopT, R1inv-R2inv, Tar-Tar*, DIS-DIS, and IncRNA54-RepZ in Escherichia coli bacteria. The method has high validity and efficiency, and it is run in low computational time in comparison to other approaches.

  2. IntaRNA 2.0: enhanced and customizable prediction of RNA-RNA interactions.

    Science.gov (United States)

    Mann, Martin; Wright, Patrick R; Backofen, Rolf

    2017-07-03

    The IntaRNA algorithm enables fast and accurate prediction of RNA-RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA-RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  3. Combinatorics of RNA-RNA interaction

    DEFF Research Database (Denmark)

    Li, Thomas J X; Reidys, Christian

    2012-01-01

    RNA-RNA binding is an important phenomenon observed for many classes of non-coding RNAs and plays a crucial role in a number of regulatory processes. Recently several MFE folding algorithms for predicting the joint structure of two interacting RNA molecules have been proposed. Here joint structure...... means that in a diagram representation the intramolecular bonds of each partner are pseudoknot-free, that the intermolecular binding pairs are noncrossing, and that there is no so-called "zigzag" configuration. This paper presents the combinatorics of RNA interaction structures including...

  4. Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

    DEFF Research Database (Denmark)

    Seemann, Ernst Stefan; Richter, Andreas S.; Gorodkin, Jan

    2010-01-01

    of that used for individual multiple alignments. Results: We derived a rather extensive algorithm. One of the advantages of our approach (in contrast to other RNARNA interaction prediction methods) is the application of covariance detection and prediction of pseudoknots between intra- and inter-molecular base...... pairs. As a proof of concept, we show an example and discuss the strengths and weaknesses of the approach....

  5. Topology of RNA-RNA interaction structures

    DEFF Research Database (Denmark)

    Andersen, Jørgen Ellegaard; Huang, Fenix Wenda; Penner, Robert

    2012-01-01

    Abstract The topological filtration of interacting RNA complexes is studied, and the role is analyzed of certain diagrams called irreducible shadows, which form suitable building blocks for more general structures. We prove that, for two interacting RNAs, called interaction structures, there exist...

  6. On RNA-RNA interaction structures of fixed topological genus.

    Science.gov (United States)

    Fu, Benjamin M M; Han, Hillary S W; Reidys, Christian M

    2015-04-01

    Interacting RNA complexes are studied via bicellular maps using a filtration via their topological genus. Our main result is a new bijection for RNA-RNA interaction structures and a linear time uniform sampling algorithm for RNA complexes of fixed topological genus. The bijection allows to either reduce the topological genus of a bicellular map directly, or to lose connectivity by decomposing the complex into a pair of single stranded RNA structures. Our main result is proved bijectively. It provides an explicit algorithm of how to rewire the corresponding complexes and an unambiguous decomposition grammar. Using the concept of genus induction, we construct bicellular maps of fixed topological genus g uniformly in linear time. We present various statistics on these topological RNA complexes and compare our findings with biological complexes. Furthermore we show how to construct loop-energy based complexes using our decomposition grammar. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. A Method to Predict the Structure and Stability of RNA/RNA Complexes.

    Science.gov (United States)

    Xu, Xiaojun; Chen, Shi-Jie

    2016-01-01

    RNA/RNA interactions are essential for genomic RNA dimerization and regulation of gene expression. Intermolecular loop-loop base pairing is a widespread and functionally important tertiary structure motif in RNA machinery. However, computational prediction of intermolecular loop-loop base pairing is challenged by the entropy and free energy calculation due to the conformational constraint and the intermolecular interactions. In this chapter, we describe a recently developed statistical mechanics-based method for the prediction of RNA/RNA complex structures and stabilities. The method is based on the virtual bond RNA folding model (Vfold). The main emphasis in the method is placed on the evaluation of the entropy and free energy for the loops, especially tertiary kissing loops. The method also uses recursive partition function calculations and two-step screening algorithm for large, complicated structures of RNA/RNA complexes. As case studies, we use the HIV-1 Mal dimer and the siRNA/HIV-1 mutant (T4) to illustrate the method.

  8. Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.

    Science.gov (United States)

    Yan, Yumeng; Wen, Zeyu; Zhang, Di; Huang, Sheng-You

    2018-05-18

    RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA-RNA interactions based on a training set of 97 diverse RNA-RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA-RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA-RNA complexes.

  9. iDoRNA: An Interacting Domain-based Tool for Designing RNA-RNA Interaction Systems

    Directory of Open Access Journals (Sweden)

    Jittrawan Thaiprasit

    2016-03-01

    Full Text Available RNA-RNA interactions play a crucial role in gene regulation in living organisms. They have gained increasing interest in the field of synthetic biology because of their potential applications in medicine and biotechnology. However, few novel regulators based on RNA-RNA interactions with desired structures and functions have been developed due to the challenges of developing design tools. Recently, we proposed a novel tool, called iDoDe, for designing RNA-RNA interacting sequences by first decomposing RNA structures into interacting domains and then designing each domain using a stochastic algorithm. However, iDoDe did not provide an optimal solution because it still lacks a mechanism to optimize the design. In this work, we have further developed the tool by incorporating a genetic algorithm (GA to find an RNA solution with maximized structural similarity and minimized hybridized RNA energy, and renamed the tool iDoRNA. A set of suitable parameters for the genetic algorithm were determined and found to be a weighting factor of 0.7, a crossover rate of 0.9, a mutation rate of 0.1, and the number of individuals per population set to 8. We demonstrated the performance of iDoRNA in comparison with iDoDe by using six RNA-RNA interaction models. It was found that iDoRNA could efficiently generate all models of interacting RNAs with far more accuracy and required far less computational time than iDoDe. Moreover, we compared the design performance of our tool against existing design tools using forty-four RNA-RNA interaction models. The results showed that the performance of iDoRNA is better than RiboMaker when considering the ensemble defect, the fitness score and computation time usage. However, it appears that iDoRNA is outperformed by NUPACK and RNAiFold 2.0 when considering the ensemble defect. Nevertheless, iDoRNA can still be an useful alternative tool for designing novel RNA-RNA interactions in synthetic biology research. The source code of i

  10. Disruption of Specific RNA-RNA Interactions in a Double-Stranded RNA Virus Inhibits Genome Packaging and Virus Infectivity.

    Science.gov (United States)

    Fajardo, Teodoro; Sung, Po-Yu; Roy, Polly

    2015-12-01

    Bluetongue virus (BTV) causes hemorrhagic disease in economically important livestock. The BTV genome is organized into ten discrete double-stranded RNA molecules (S1-S10) which have been suggested to follow a sequential packaging pathway from smallest to largest segment during virus capsid assembly. To substantiate and extend these studies, we have investigated the RNA sorting and packaging mechanisms with a new experimental approach using inhibitory oligonucleotides. Putative packaging signals present in the 3'untranslated regions of BTV segments were targeted by a number of nuclease resistant oligoribonucleotides (ORNs) and their effects on virus replication in cell culture were assessed. ORNs complementary to the 3' UTR of BTV RNAs significantly inhibited virus replication without affecting protein synthesis. Same ORNs were found to inhibit complex formation when added to a novel RNA-RNA interaction assay which measured the formation of supramolecular complexes between and among different RNA segments. ORNs targeting the 3'UTR of BTV segment 10, the smallest RNA segment, were shown to be the most potent and deletions or substitution mutations of the targeted sequences diminished the RNA complexes and abolished the recovery of viable viruses using reverse genetics. Cell-free capsid assembly/RNA packaging assay also confirmed that the inhibitory ORNs could interfere with RNA packaging and further substitution mutations within the putative RNA packaging sequence have identified the recognition sequence concerned. Exchange of 3'UTR between segments have further demonstrated that RNA recognition was segment specific, most likely acting as part of the secondary structure of the entire genomic segment. Our data confirm that genome packaging in this segmented dsRNA virus occurs via the formation of supramolecular complexes formed by the interaction of specific sequences located in the 3' UTRs. Additionally, the inhibition of packaging in-trans with inhibitory ORNs

  11. 5'-3' RNA-RNA interaction facilitates cap- and poly(A) tail-independent translation of tomato bushy stunt virus mrna: a potential common mechanism for tombusviridae.

    Science.gov (United States)

    Fabian, Marc R; White, K Andrew

    2004-07-09

    Tomato bushy stunt virus (TBSV) is the prototypical member of the genus Tombusvirus in the family Tombusviridae. The (+)-strand RNA genome of TBSV lacks both a 5' cap and a 3' poly(A) tail and instead contains a 3'-terminal RNA sequence that acts as a cap-independent translational enhancer (3' CITE). In this study, we have determined the RNA secondary structure of the translation-specific central segment of the 3' CITE, termed region 3.5 (R3.5). MFOLD structural modeling combined with solution structure mapping and comparative sequence analysis indicate that R3.5 adopts a branched structure that contains three major helices. Deletion and substitution studies revealed that two of these extended stem-loop (SL) structures are essential for 3' CITE activity in vivo. In particular, the terminal loop of one of these SLs, SL-B, was found to be critical for translation. Compensatory mutational analysis showed that SL-B functions by base pairing with another SL, SL3, in the 5' untranslated region of the TBSV genome. Thus, efficient translation of TBSV mRNA in vivo requires a 5'-3' RNA-RNA interaction that effectively circularizes the message. Similar types of interactions are also predicted to occur in TBSV subgenomic mRNAs between their 5' untranslated regions and the 3' CITE, and both genomic and subgenomic 5'-3' interactions are well conserved in all members of the genus Tombusvirus. In addition, a survey of other genera in Tombusviridae revealed the potential for similar 5'-3' RNA-RNA-based interactions in their viral mRNAs, suggesting that this mechanism extends throughout this large virus family.

  12. Efficient Translation of Pelargonium line pattern virus RNAs Relies on a TED-Like 3´-Translational Enhancer that Communicates with the Corresponding 5´-Region through a Long-Distance RNA-RNA Interaction.

    Science.gov (United States)

    Blanco-Pérez, Marta; Pérez-Cañamás, Miryam; Ruiz, Leticia; Hernández, Carmen

    2016-01-01

    Cap-independent translational enhancers (CITEs) have been identified at the 3´-terminal regions of distinct plant positive-strand RNA viruses belonging to families Tombusviridae and Luteoviridae. On the bases of their structural and/or functional requirements, at least six classes of CITEs have been defined whose distribution does not correlate with taxonomy. The so-called TED class has been relatively under-studied and its functionality only confirmed in the case of Satellite tobacco necrosis virus, a parasitic subviral agent. The 3´-untranslated region of the monopartite genome of Pelargonium line pattern virus (PLPV), the recommended type member of a tentative new genus (Pelarspovirus) in the family Tombusviridae, was predicted to contain a TED-like CITE. Similar CITEs can be anticipated in some other related viruses though none has been experimentally verified. Here, in the first place, we have performed a reassessment of the structure of the putative PLPV-TED through in silico predictions and in vitro SHAPE analysis with the full-length PLPV genome, which has indicated that the presumed TED element is larger than previously proposed. The extended conformation of the TED is strongly supported by the pattern of natural sequence variation, thus providing comparative structural evidence in support of the structural data obtained by in silico and in vitro approaches. Next, we have obtained experimental evidence demonstrating the in vivo activity of the PLPV-TED in the genomic (g) RNA, and also in the subgenomic (sg) RNA that the virus produces to express 3´-proximal genes. Besides other structural features, the results have highlighted the key role of long-distance kissing-loop interactions between the 3´-CITE and 5´-proximal hairpins for gRNA and sgRNA translation. Bioassays of CITE mutants have confirmed the importance of the identified 5´-3´ RNA communication for viral infectivity and, moreover, have underlined the strong evolutionary constraints that may

  13. Efficient Translation of Pelargonium line pattern virus RNAs Relies on a TED-Like 3´-Translational Enhancer that Communicates with the Corresponding 5´-Region through a Long-Distance RNA-RNA Interaction.

    Directory of Open Access Journals (Sweden)

    Marta Blanco-Pérez

    Full Text Available Cap-independent translational enhancers (CITEs have been identified at the 3´-terminal regions of distinct plant positive-strand RNA viruses belonging to families Tombusviridae and Luteoviridae. On the bases of their structural and/or functional requirements, at least six classes of CITEs have been defined whose distribution does not correlate with taxonomy. The so-called TED class has been relatively under-studied and its functionality only confirmed in the case of Satellite tobacco necrosis virus, a parasitic subviral agent. The 3´-untranslated region of the monopartite genome of Pelargonium line pattern virus (PLPV, the recommended type member of a tentative new genus (Pelarspovirus in the family Tombusviridae, was predicted to contain a TED-like CITE. Similar CITEs can be anticipated in some other related viruses though none has been experimentally verified. Here, in the first place, we have performed a reassessment of the structure of the putative PLPV-TED through in silico predictions and in vitro SHAPE analysis with the full-length PLPV genome, which has indicated that the presumed TED element is larger than previously proposed. The extended conformation of the TED is strongly supported by the pattern of natural sequence variation, thus providing comparative structural evidence in support of the structural data obtained by in silico and in vitro approaches. Next, we have obtained experimental evidence demonstrating the in vivo activity of the PLPV-TED in the genomic (g RNA, and also in the subgenomic (sg RNA that the virus produces to express 3´-proximal genes. Besides other structural features, the results have highlighted the key role of long-distance kissing-loop interactions between the 3´-CITE and 5´-proximal hairpins for gRNA and sgRNA translation. Bioassays of CITE mutants have confirmed the importance of the identified 5´-3´ RNA communication for viral infectivity and, moreover, have underlined the strong evolutionary

  14. Genetic relatedness of orbiviruses by RNA-RNA blot hybridization

    International Nuclear Information System (INIS)

    Bodkin, D.K.

    1985-01-01

    RNA-RNA blot hybridization was developed in order to identify type-specific genes among double-stranded (ds) RNA viruses, to assess the genetic relatedness of dsRNA viruses and to classify new strains. Viral dsRNA segments were electrophoresed through 10% polyacrylamide gels, transferred to membranes, and hybridized to [5' 32 P]-pCp labeled genomic RNA from a related strain. Hybridization was performed at 52 0 C, 50% formamide, 5X SSC. Under these conditions heterologous RNA species must share ≥ 74% sequence homology in order to form stable dsRNA hybrids. Cognate genes of nine members of the Palyam serogroup of orbiviruses were identified and their sequence relatedness to the prototype. Palyam virus, was determined. Reciprocal blot hybridizations were performed using radiolabeled genomic RNA of all members of the Palyam serogroup. Unique and variant genes were identified by lack of cross-homology or by weak homology between segments. Since genes 2 and 6 exhibited the highest degree of sequence variability, response to the vertebrate immune system may be a major cause of sequence divergence among members of a single serogroup. Changuinola serogroup isolates were compared by dot-blot hybridization, while Colorado tick fever (CTF) serogroup isolates were compared by the RNA-RNA blot hybridization procedure described for reovirus and Palyam serogroup isolates. Preliminary blot hybridization data were also obtained on the relatedness of members of different Orbivirus serogroups

  15. Analysis of intermolecular RNA-RNA recombination by rubella virus

    International Nuclear Information System (INIS)

    Adams, Sandra D.; Tzeng, W.-P.; Chen, M.-H.; Frey, Teryl K.

    2003-01-01

    To investigate whether rubella virus (RUB) undergoes intermolecular RNA-RNA recombination, cells were cotransfected with pairs of in vitro transcripts from genomic cDNA plasmid vectors engineered to contain nonoverlapping deletions: the replicative transcript maintained the 5'-proximal nonstructural (NS) ORF (which contained the replicase, making it RNA replication competent), had a deletion in the 3'-proximal structural protein (SP) ORF, and maintained the 3' end of the genome, including the putative 3' cis-acting elements (CSE), while the nonreplicative transcript consisted of the 3' half of the genome including the SP-ORF and 3' CSE. Cotransfection yielded plaque-forming virus that synthesized the standard genomic and subgenomic RNAs and thus was generated by RNA-RNA recombination. Using transcripts tagged with a 3'-terminal deletion, it was found that recombinants contained the 3' end derived from the replicative strand, indicating a cis-preference for initiation of negative-strand synthesis. In cotransfections in which the replicative transcript lacked the 3' CSE, recombination occurred, albeit at lower efficiency, indicating that initiation in trans from the NS-ORF can occur. The 3' CSE was sufficient as a nonreplicative transcript, showing that it can serve as a promoter for negative-strand RNA synthesis. While deletion mutagenesis showed that the presence of the junction untranslated region (J-UTR) between the ORFs appeared to be necessary on both transcripts for recombination in this region of the genome, analysis with transcripts tagged with restriction sites showed that the J-UTR was not a hot spot for recombination compared to neighboring regions in both ORFs. Sequence analysis of recombinants revealed that both precise (homologous) and imprecise recombination (aberrant, homologous resulting in duplications) occurred; however, imprecise recombination only involved the J-UTR or the 3' end of the NS-ORF and the J-UTR (maintaining the NS-ORF), indicating

  16. Deep Predictive Models in Interactive Music

    OpenAIRE

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  17. Predictive Systems for Customer Interactions

    Science.gov (United States)

    Vijayaraghavan, Ravi; Albert, Sam; Singh, Vinod Kumar; Kannan, Pallipuram V.

    With the coming of age of web as a mainstream customer service channel, B2C companies have invested substantial resources in enhancing their web presence. Today customers can interact with a company, not only through the traditional phone channel but also through chat, email, SMS or web self-service. Each of these channels is best suited for some services and ill-matched for others. Customer service organizations today struggle with the challenge of delivering seamlessly integrated services through these different channels. This paper will evaluate some of the key challenges in multi-channel customer service. It will address the challenge of creating the right channel mix i.e. providing the right choice of channels for a given customer/behavior/issue profile. It will also provide strategies for optimizing the performance of a given channel in creating the right customer experience.

  18. Gaussian interaction profile kernels for predicting drug-target interaction.

    Science.gov (United States)

    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  19. PREDICTING RELEVANT EMPTY SPOTS IN SOCIAL INTERACTION

    Institute of Scientific and Technical Information of China (English)

    Yoshiharu MAENO; Yukio OHSAWA

    2008-01-01

    An empty spot refers to an empty hard-to-fill space which can be found in the records of the social interaction, and is the clue to the persons in the underlying social network who do not appear in the records. This contribution addresses a problem to predict relevant empty spots in social interaction. Homogeneous and inhomogeneous networks are studied as a model underlying the social interaction. A heuristic predictor function method is presented as a new method to address the problem. Simulation experiment is demonstrated over a homogeneous network. A test data set in the form of market baskets is generated from the simulated communication. Precision to predict the empty spots is calculated to demonstrate the performance of the presented method.

  20. Improving LMA predictions with non standard interactions

    CERN Document Server

    Das, C R

    2010-01-01

    It has been known for some time that the well established LMA solution to the observed solar neutrino deficit fails to predict a flat energy spectrum for SuperKamiokande as opposed to what the data indicates. It also leads to a Chlorine rate which appears to be too high as compared to the data. We investigate the possible solution to these inconsistencies with non standard neutrino interactions, assuming that they come as extra contributions to the $\

  1. Interaction features for prediction of perceptual segmentation

    DEFF Research Database (Denmark)

    Hartmann, Martin; Lartillot, Olivier; Toiviainen, Petri

    2017-01-01

    As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise...... and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners...... was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians...

  2. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  3. 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.

  4. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  5. Predicting community composition from pairwise interactions

    Science.gov (United States)

    Friedman, Jonathan; Higgins, Logan; Gore, Jeff

    The ability to predict the structure of complex, multispecies communities is crucial for understanding the impact of species extinction and invasion on natural communities, as well as for engineering novel, synthetic communities. Communities are often modeled using phenomenological models, such as the classical generalized Lotka-Volterra (gLV) model. While a lot of our intuition comes from such models, their predictive power has rarely been tested experimentally. To directly assess the predictive power of this approach, we constructed synthetic communities comprised of up to 8 soil bacteria. We measured the outcome of competition between all species pairs, and used these measurements to predict the composition of communities composed of more than 2 species. The pairwise competitions resulted in a diverse set of outcomes, including coexistence, exclusion, and bistability, and displayed evidence for both interference and facilitation. Most pair outcomes could be captured by the gLV framework, and the composition of multispecies communities could be predicted for communities composed solely of such pairs. Our results demonstrate the predictive ability and utility of simple phenomenology, which enables accurate predictions in the absence of mechanistic details.

  6. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  7. Predicting drug-target interactions using restricted Boltzmann machines.

    Science.gov (United States)

    Wang, Yuhao; Zeng, Jianyang

    2013-07-01

    In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

  8. Drug-Target Interactions: Prediction Methods and Applications.

    Science.gov (United States)

    Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

    2018-01-01

    Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  10. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

    Science.gov (United States)

    Ezzat, Ali; Zhao, Peilin; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2017-01-01

    Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.

  12. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    Science.gov (United States)

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

  13. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    Directory of Open Access Journals (Sweden)

    Ji-Wei Chang

    2016-11-01

    Full Text Available Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

  14. Information assessment on predicting protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Gerstein Mark

    2004-10-01

    Full Text Available Abstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the

  15. Predicting behavior during interracial interactions: a stress and coping approach.

    Science.gov (United States)

    Trawalter, Sophie; Richeson, Jennifer A; Shelton, J Nicole

    2009-11-01

    The social psychological literature maintains unequivocally that interracial contact is stressful. Yet research and theory have rarely considered how stress may shape behavior during interracial interactions. To address this empirical and theoretical gap, the authors propose a framework for understanding and predicting behavior during interracial interactions rooted in the stress and coping literature. Specifically, they propose that individuals often appraise interracial interactions as a threat, experience stress, and therefore cope-they antagonize, avoid, freeze, or engage. In other words, the behavioral dynamics of interracial interactions can be understood as initial stress reactions and subsequent coping responses. After articulating the framework and its predictions for behavior during interracial interactions, the authors examine its ability to organize the extant literature on behavioral dynamics during interracial compared with same-race contact. They conclude with a discussion of the implications of the stress and coping framework for improving research and fostering more positive interracial contact.

  16. Predicting rates of interspecific interaction from phylogenetic trees.

    Science.gov (United States)

    Nuismer, Scott L; Harmon, Luke J

    2015-01-01

    Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

  17. Deep-Learning-Based Drug-Target Interaction Prediction.

    Science.gov (United States)

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  18. Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Zheng Sun

    2014-01-01

    Full Text Available WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1 and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA, two integrin beta (ITGB, and one syndecan (SDC. Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

  19. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-03-16

    Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind

  20. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  1. Assessment of CFD capability for prediction of hypersonic shock interactions

    Science.gov (United States)

    Knight, Doyle; Longo, José; Drikakis, Dimitris; Gaitonde, Datta; Lani, Andrea; Nompelis, Ioannis; Reimann, Bodo; Walpot, Louis

    2012-01-01

    The aerothermodynamic loadings associated with shock wave boundary layer interactions (shock interactions) must be carefully considered in the design of hypersonic air vehicles. The capability of Computational Fluid Dynamics (CFD) software to accurately predict hypersonic shock wave laminar boundary layer interactions is examined. A series of independent computations performed by researchers in the US and Europe are presented for two generic configurations (double cone and cylinder) and compared with experimental data. The results illustrate the current capabilities and limitations of modern CFD methods for these flows.

  2. Interactive Translation Prediction versus Conventional Post-editing in Practice

    DEFF Research Database (Denmark)

    Sanchis-Trilles, German; Alabau, Vicent; Buck, Christian

    2014-01-01

    We conducted a field trial in computer-assisted professional translation to compare Interactive Translation Prediction (ITP) against conventional post- editing (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translatio...

  3. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

    Directory of Open Access Journals (Sweden)

    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  4. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  5. Plant interactions alter the predictions of metabolic scaling theory.

    Directory of Open Access Journals (Sweden)

    Yue Lin

    Full Text Available Metabolic scaling theory (MST is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning. Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric, and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

  6. Big Five aspects of personality interact to predict depression.

    Science.gov (United States)

    Allen, Timothy A; Carey, Bridget E; McBride, Carolina; Bagby, R Michael; DeYoung, Colin G; Quilty, Lena C

    2017-09-16

    Research has shown that three personality traits-Neuroticism, Extraversion, and Conscientiousness-moderate one another in a three-way interaction that predicts depressive symptoms in healthy populations. We test the hypothesis that this effect is driven by three lower-order traits: withdrawal, industriousness, and enthusiasm. We then replicate this interaction within a clinical population for the first time. Sample 1 included 376 healthy adults. Sample 2 included 354 patients diagnosed with current major depressive disorder. Personality and depressive tendencies were assessed via the Big Five Aspect Scales and Personality Inventory for DSM-5 in Sample 1, respectively, and by the NEO-PI-R and Beck Depression Inventory-II in Sample 2. Withdrawal, industriousness, and enthusiasm interacted to predict depressive tendencies in both samples. The pattern of the interaction supported a "best two out of three" principle, in which low risk scores on two trait dimensions protects against a high risk score on the third trait. Evidence was also present for a "worst two out of three" principle, in which high risk scores on two traits are associated with equivalent depressive severity as high risk scores on all three traits. These results highlight the importance of examining interactive effects of personality traits on psychopathology. © 2017 Wiley Periodicals, Inc.

  7. Herb-drug interactions: challenges and opportunities for improved predictions.

    Science.gov (United States)

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

    Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

  8. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    Science.gov (United States)

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  9. Attachment predicts cortisol response and closeness in dyadic social interaction.

    Science.gov (United States)

    Ketay, Sarah; Beck, Lindsey A

    2017-06-01

    The present study examined how the interplay of partners' attachment styles influences cortisol response, actual closeness, and desired closeness during friendship initiation. Participants provided salivary cortisol samples at four timepoints throughout either a high or low closeness task that facilitated high or low levels of self-disclosure with a potential friend (i.e., another same-sex participant). Levels of actual closeness and desired closeness following the task were measured via inclusion of other in the self. Results from multi-level modeling indicated that the interaction of both participants' attachment avoidance predicted cortisol response patterns, with participants showing the highest cortisol response when there was a mismatch between their own and their partners' attachment avoidance. Further, the interaction between both participants' attachment anxiety predicted actual closeness and desired closeness, with participants both feeling and wanting the most closeness with partners when both they and their partners were low in attachment anxiety. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Boosting compound-protein interaction prediction by deep learning.

    Science.gov (United States)

    Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

    2016-11-01

    The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Prediction of rotor blade-vortex interaction using Volterra integrals

    Energy Technology Data Exchange (ETDEWEB)

    Wong, A.; Nitzsche, F. [Carleton Univ., Dept. of Mechanical and Aerospace Engineering, Ottawa, Ontario (Canada)]. E-mail: Fred_Nitzsche@carleton.ca; Khalid, M. [National Research Council Canada, Inst. for Aerospace Research, Ottawa, Ontario (Canada)

    2004-07-01

    The theory of Volterra integral equations for nonlinear system is applied to the prediction of the nonlinear aerodynamic response of an NACA 0012 airfoil experiencing blade-vortex interaction. The phenomenon is first modeled in two-dimensions using an Euler/Navier-Stoke code, and the resulting unsteady aerodynamic flow field sequences are appropriately combined to form a training dataset. The Volterra kernels are identified in the time-domain characteristics of the selected data, which is in turn used to predict the nonlinear aerodynamic response of the airfoil. The Volterra kernel based data is then compared against a standard airfoil response. The predicted lift time histories of the airfoil are shown to be in good agreement with the aerodynamic data. (author)

  12. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail; Soufan, Othman; Essack, Magbubah; Kalnis, Panos; Bajic, Vladimir B.

    2016-01-01

    DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://​www.​cbrc.​kaust.​edu.​sa/​daspfind.

  13. Prediction of rotor blade-vortex interaction using Volterra integrals

    International Nuclear Information System (INIS)

    Wong, A.; Nitzsche, F.; Khalid, M.

    2004-01-01

    The theory of Volterra integral equations for nonlinear system is applied to the prediction of the nonlinear aerodynamic response of an NACA 0012 airfoil experiencing blade-vortex interaction. The phenomenon is first modeled in two-dimensions using an Euler/Navier-Stoke code, and the resulting unsteady aerodynamic flow field sequences are appropriately combined to form a training dataset. The Volterra kernels are identified in the time-domain characteristics of the selected data, which is in turn used to predict the nonlinear aerodynamic response of the airfoil. The Volterra kernel based data is then compared against a standard airfoil response. The predicted lift time histories of the airfoil are shown to be in good agreement with the aerodynamic data. (author)

  14. Drug-target interaction prediction: A Bayesian ranking approach.

    Science.gov (United States)

    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  15. QSAR Modeling and Prediction of Drug-Drug Interactions.

    Science.gov (United States)

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  16. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

  17. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Directory of Open Access Journals (Sweden)

    Jaime Cuevas

    2017-01-01

    Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u   and   f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .

  18. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

    2017-01-05

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

  19. Plant interactions alter the predictions of metabolic scaling theory

    DEFF Research Database (Denmark)

    Lin, Yue; Berger, Uta; Grimm, Volker

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of 24/3 between mean individual biomass and density during densitydependent mortality (self-thinning). Empirical tests have...... processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive....... of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories...

  20. Explicit and Implicit Approach Motivation Interact to Predict Interpersonal Arrogance

    Science.gov (United States)

    Robinson, Michael D.; Ode, Scott; Spencer L., Palder; Fetterman, Adam K.

    2012-01-01

    Self-reports of approach motivation are unlikely to be sufficient in understanding the extent to which the individual reacts to appetitive cues in an approach-related manner. A novel implicit probe of approach tendencies was thus developed, one that assessed the extent to which positive affective (versus neutral) stimuli primed larger size estimates, as larger perceptual sizes co-occur with locomotion toward objects in the environment. In two studies (total N = 150), self-reports of approach motivation interacted with this implicit probe of approach motivation to predict individual differences in arrogance, a broad interpersonal dimension previously linked to narcissism, antisocial personality tendencies, and aggression. The results of the two studies were highly parallel in that self-reported levels of approach motivation predicted interpersonal arrogance in the particular context of high, but not low, levels of implicit approach motivation. Implications for understanding approach motivation, implicit probes of it, and problematic approach-related outcomes are discussed. PMID:22399360

  1. Explicit and implicit approach motivation interact to predict interpersonal arrogance.

    Science.gov (United States)

    Robinson, Michael D; Ode, Scott; Palder, Spencer L; Fetterman, Adam K

    2012-07-01

    Self-reports of approach motivation are unlikely to be sufficient in understanding the extent to which the individual reacts to appetitive cues in an approach-related manner. A novel implicit probe of approach tendencies was thus developed, one that assessed the extent to which positive affective (versus neutral) stimuli primed larger size estimates, as larger perceptual sizes co-occur with locomotion toward objects in the environment. In two studies (total N = 150), self-reports of approach motivation interacted with this implicit probe of approach motivation to predict individual differences in arrogance, a broad interpersonal dimension previously linked to narcissism, antisocial personality tendencies, and aggression. The results of the two studies were highly parallel in that self-reported levels of approach motivation predicted interpersonal arrogance in the particular context of high, but not low, levels of implicit approach motivation. Implications for understanding approach motivation, implicit probes of it, and problematic approach-related outcomes are discussed.

  2. Prediction of localization and interactions of apoptotic proteins

    Directory of Open Access Journals (Sweden)

    Matula Pavel

    2009-07-01

    Full Text Available Abstract During apoptosis several mitochondrial proteins are released. Some of them participate in caspase-independent nuclear DNA degradation, especially apoptosis-inducing factor (AIF and endonuclease G (endoG. Another interesting protein, which was expected to act similarly as AIF due to the high sequence homology with AIF is AIF-homologous mitochondrion-associated inducer of death (AMID. We studied the structure, cellular localization, and interactions of several proteins in silico and also in cells using fluorescent microscopy. We found the AMID protein to be cytoplasmic, most probably incorporated into the cytoplasmic side of the lipid membranes. Bioinformatic predictions were conducted to analyze the interactions of the studied proteins with each other and with other possible partners. We conducted molecular modeling of proteins with unknown 3D structures. These models were then refined by MolProbity server and employed in molecular docking simulations of interactions. Our results show data acquired using a combination of modern in silico methods and image analysis to understand the localization, interactions and functions of proteins AMID, AIF, endonuclease G, and other apoptosis-related proteins.

  3. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  4. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  5. Predicting interactions from mechanistic information: Can omic data validate theories?

    International Nuclear Information System (INIS)

    Borgert, Christopher J.

    2007-01-01

    To address the most pressing and relevant issues for improving mixture risk assessment, researchers must first recognize that risk assessment is driven by both regulatory requirements and scientific research, and that regulatory concerns may expand beyond the purely scientific interests of researchers. Concepts of 'mode of action' and 'mechanism of action' are used in particular ways within the regulatory arena, depending on the specific assessment goals. The data requirements for delineating a mode of action and predicting interactive toxicity in mixtures are not well defined from a scientific standpoint due largely to inherent difficulties in testing certain underlying assumptions. Understanding the regulatory perspective on mechanistic concepts will be important for designing experiments that can be interpreted clearly and applied in risk assessments without undue reliance on extrapolation and assumption. In like fashion, regulators and risk assessors can be better equipped to apply mechanistic data if the concepts underlying mechanistic research and the limitations that must be placed on interpretation of mechanistic data are understood. This will be critically important for applying new technologies to risk assessment, such as functional genomics, proteomics, and metabolomics. It will be essential not only for risk assessors to become conversant with the language and concepts of mechanistic research, including new omic technologies, but also, for researchers to become more intimately familiar with the challenges and needs of risk assessment

  6. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  7. Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

    Science.gov (United States)

    Wuchty, Stefan

    2006-05-23

    While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

  8. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  9. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  10. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

    2013-01-27

    A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

  11. 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.

  12. Predicting and detecting reciprocity between indirect ecological interactions and evolution.

    Science.gov (United States)

    Estes, James A; Brashares, Justin S; Power, Mary E

    2013-05-01

    Living nature can be thought of as a tapestry, defined not only by its constituent parts but also by how these parts are woven together. The weaving of this tapestry is a metaphor for species interactions, which can be divided into three broad classes: competitive, mutualistic, and consumptive. Direct interactions link together as more complex networks, for example, the joining of consumptive interactions into food webs. Food web dynamics are driven, in turn, by changes in the abundances of web members, whose numbers or biomass respond to bottom-up (resource limitation) and top-down (consumer limitation) forcing. The relative strengths of top-down and bottom-up forcing on the abundance of a given web member depend on its ecological context, including its topological position within the food web. Top-down effects by diverse consumers are nearly ubiquitous, in many cases influencing the structure and operation of ecosystems. While the ecological effects of such interactions are well known, far less is known of their evolutionary consequences. In this essay, we describe sundry consequences of these interaction chains on species and ecosystem processes, explain several known or suspected evolutionary effects of consumer-induced interaction chains, and identify areas where reciprocity between ecology and evolution involving the indirect effects of consumer-prey interaction chains might be further explored.

  13. Drug-target interaction prediction via class imbalance-aware ensemble learning.

    Science.gov (United States)

    Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2016-12-22

    Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

  14. Working and Playing Together: Prediction of Preschool Social-Emotional Competence from Mother-Child Interaction.

    Science.gov (United States)

    Denham, Susanne A.; And Others

    1991-01-01

    Examined mother-child interaction in play and teaching tasks. Mother-child interaction aggregates represented task orientation, positive emotion, and allowance of autonomy. Maternal interaction aggregates predicted teachers' ratings of children's positive social behavior, assertiveness, and sadness in the preschool setting. (BC)

  15. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    ABSTRACT: BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  16. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions.

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  17. Social interactions predict genetic diversification: an experimental manipulation in shorebirds.

    Science.gov (United States)

    Cunningham, Charles; Parra, Jorge E; Coals, Lucy; Beltrán, Marcela; Zefania, Sama; Székely, Tamás

    2018-01-01

    Mating strategy and social behavior influence gene flow and hence affect levels of genetic differentiation and potentially speciation. Previous genetic analyses of closely related plovers Charadrius spp. found strikingly different population genetic structure in Madagascar: Kittlitz's plovers are spatially homogenous whereas white-fronted plovers have well segregated and geographically distinct populations. Here, we test the hypotheses that Kittlitz's plovers are spatially interconnected and have extensive social interactions that facilitate gene flow, whereas white-fronted plovers are spatially discrete and have limited social interactions. By experimentally removing mates from breeding pairs and observing the movements of mate-searching plovers in both species, we compare the spatial behavior of Kittlitz's and white-fronted plovers within a breeding season. The behavior of experimental birds was largely consistent with expectations: Kittlitz's plovers travelled further, sought new mates in larger areas, and interacted with more individuals than white-fronted plovers, however there was no difference in breeding dispersal. These results suggest that mating strategies, through spatial behavior and social interactions, are predictors of gene flow and thus genetic differentiation and speciation. Our study highlights the importance of using social behavior to understand gene flow. However, further work is needed to investigate the relative importance of social structure, as well as intra- and inter-season dispersal, in influencing the genetic structures of populations.

  18. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

    Directory of Open Access Journals (Sweden)

    Mile Sikić

    2009-01-01

    Full Text Available Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i a combination of sequence- and structure-derived parameters and (ii sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

  19. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

    Science.gov (United States)

    Zhang, Wen; Chen, Yanlin; Li, Dingfang

    2017-11-25

    Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

  20. Simplified method to predict mutual interactions of human transcription factors based on their primary structure

    KAUST Repository

    Schmeier, Sebastian

    2011-07-05

    Background: Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. Methodology: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account. © 2011 Schmeier et al.

  1. Simplified method to predict mutual interactions of human transcription factors based on their primary structure.

    Directory of Open Access Journals (Sweden)

    Sebastian Schmeier

    Full Text Available BACKGROUND: Physical interactions between transcription factors (TFs are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. METHODOLOGY: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus and in the same type of molecular process (transcription initiation. Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. CONCLUSIONS: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account.

  2. Predicting Nanocrystal Shape through Consideration of Surface-Ligand Interactions

    KAUST Repository

    Bealing, Clive R.

    2012-03-27

    Density functional calculations for the binding energy of oleic acid-based ligands on Pb-rich {100} and {111} facets of PbSe nanocrystals determine the surface energies as a function of ligand coverage. Oleic acid is expected to bind to the nanocrystal surface in the form of lead oleate. The Wulff construction predicts the thermodynamic equilibrium shape of the PbSe nanocrystals. The equilibrium shape is a function of the ligand surface coverage, which can be controlled by changing the concentration of oleic acid during synthesis. The different binding energy of the ligand on the {100} and {111} facets results in different equilibrium ligand coverages on the facets, and a transition in the equilibrium shape from octahedral to cubic is predicted when increasing the ligand concentration during synthesis. © 2012 American Chemical Society.

  3. Integrated Predictive Models for ICRF-Edge Plasma Interactions

    International Nuclear Information System (INIS)

    Daniel A. D'Ippolito

    2005-01-01

    The coupling of radiofrequency waves to the edge plasma of a fusion device produces strong nonlinear interactions with the plasma and surrounding material walls which must be controlled in order to protect the antenna and to obtain efficient heating of the core plasma. The goal of the STTR project was to develop the first quantitative numerical simulation of this problem. This report describes the results of the Phase I work by Lodestar and ORNL on this project

  4. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    2017-01-01

    is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...

  5. Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs.

    Science.gov (United States)

    Huo, Tong; Liu, Wei; Guo, Yu; Yang, Cheng; Lin, Jianping; Rao, Zihe

    2015-03-26

    Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease. We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by 'interolog' method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins. This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

  6. Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

    DEFF Research Database (Denmark)

    Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

    2012-01-01

    Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

  7. Method of predicting Splice Sites based on signal interactions

    Directory of Open Access Journals (Sweden)

    Deogun Jitender S

    2006-04-01

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

  8. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

    Science.gov (United States)

    Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

    2015-01-01

    The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

  9. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

    Directory of Open Access Journals (Sweden)

    Ching-Tai Chen

    Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted

  10. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    Science.gov (United States)

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

  11. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng

    2012-01-01

    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  12. Monte Carlo prediction of neutron interactions in sonofusion experiment

    International Nuclear Information System (INIS)

    Walter, J.; Gert, G.; Bougaev, A.; Bertodano, B.; Tsoukalas, I.H.; Jevremovic, T. . E-mail address of corresponding author: tatjanaj@ecn.purdue.edu

    2005-01-01

    Evidence of neutron induced sonofusion has been reported by Taleyarkhan, et. al, (Science, 8 March 2002). This involves the creation and collapse of cavities with acoustic waves and neutrons in deuterated acetone. The collapse of these bubbles creates conditions sufficient for D-D fusion to occur. As part of a bigger effort to reproduce these results, the neutral condition (without the acoustic waves) case was considered. This limits the neutron interactions to scattering and attenuation. MCNP5 was used to simulate the experiment for this neutral case. The set-up consisted of a cylindrical glass vessel that contained 500 mL of 99.9% D-acetone that was exposed to a 9.70 Ci Americium Beryllium neutron source. MCNP5 gave a production rate of 4.99E-11 (Relative Error: +/- 0.0005) tritons per source neutron for neutron absorption in deuterium. The resulting simulation's tritium activity was corrected for decay and detector efficiency, then compared to the actual experimental results. (author)

  13. Conflict and expectancies interact to predict sexual behavior under the influence among gay and bisexual men

    Science.gov (United States)

    Wells, Brooke E; Starks, Tyrel J; Parsons, Jeffrey T; Golub, Sarit

    2013-01-01

    As the mechanisms of the associations between substance use and risky sex remain unclear, this study investigates the interactive roles of conflicts about casual sex and condom use and expectancies of the sexual effects of substances in those associations among gay men. Conflict interacted with expectancies to predict sexual behavior under the influence; low casual sex conflict coupled with high expectancies predicted the highest number of casual partners, and high condom use conflict and high expectancies predicted the highest number of unprotected sex acts. Results have implications for intervention efforts that aim to improve sexual decision-making and reduce sexual expectancies. PMID:23584507

  14. Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

    Science.gov (United States)

    Liu, Zhi-Ping; Chen, Luonan

    2016-01-01

    Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

  15. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  16. Enhanced effects of biotic interactions on predicting multispecies spatial distribution of submerged macrophytes after eutrophication.

    Science.gov (United States)

    Song, Kun; Cui, Yichong; Zhang, Xijin; Pan, Yingji; Xu, Junli; Xu, Kaiqin; Da, Liangjun

    2017-10-01

    Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model (GLM) and boosted regression trees (BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian-based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co-occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes' distribution under eutrophication stress.

  17. IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

    Science.gov (United States)

    Mann, Martin; Wright, Patrick R.

    2017-01-01

    Abstract The IntaRNA algorithm enables fast and accurate prediction of RNA–RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA–RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. PMID:28472523

  18. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  19. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

    Science.gov (United States)

    Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

    2013-04-01

    Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.

  20. Data-driven prediction of adverse drug reactions induced by drug-drug interactions.

    Science.gov (United States)

    Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Kumar, Kamal; Yu, Xueping; Wallqvist, Anders; Reifman, Jaques

    2017-06-08

    The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects. We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles. We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org . We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs

  1. Enhancing the prediction of protein pairings between interacting families using orthology information

    Directory of Open Access Journals (Sweden)

    Pazos Florencio

    2008-01-01

    Full Text Available Abstract Background It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other. The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. Results For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. Conclusion Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.

  2. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  3. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  4. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio; Cannistraci, Carlo; Ravasi, Timothy

    2013-01-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  5. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

    Science.gov (United States)

    Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

    2016-10-01

    Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

  7. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

    Directory of Open Access Journals (Sweden)

    Gomez Shawn M

    2010-04-01

    Full Text Available Abstract Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

  8. The role of gene-gene interaction in the prediction of criminal behavior.

    Science.gov (United States)

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

    2014-04-01

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

  9. COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

    Science.gov (United States)

    Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

    2016-01-01

    Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

  10. Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

    International Nuclear Information System (INIS)

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-01-01

    The vast majority of the chores in the living cell involve protein–protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein–protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations

  11. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo

    2013-06-21

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The

  12. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  13. Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

    Science.gov (United States)

    van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A

    2018-02-01

    This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.

  14. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

    Science.gov (United States)

    Liu, Yong; Wu, Min; Miao, Chunyan; Zhao, Peilin; Li, Xiao-Li

    2016-02-01

    In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

  15. Prediction of interface residue based on the features of residue interaction network.

    Science.gov (United States)

    Jiao, Xiong; Ranganathan, Shoba

    2017-11-07

    Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. InterProSurf: a web server for predicting interacting sites on protein surfaces

    Science.gov (United States)

    Negi, Surendra S.; Schein, Catherine H.; Oezguen, Numan; Power, Trevor D.; Braun, Werner

    2009-01-01

    Summary A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. PMID:17933856

  17. DomPep--a general method for predicting modular domain-mediated protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Lei Li

    Full Text Available Protein-protein interactions (PPIs are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.

  18. Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Julian Zubek

    2015-07-01

    Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

  19. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  20. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  1. Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

    Science.gov (United States)

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-11-21

    Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

    Science.gov (United States)

    Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

    2012-07-01

    Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

  3. Is racial bias malleable? Whites' lay theories of racial bias predict divergent strategies for interracial interactions.

    Science.gov (United States)

    Neel, Rebecca; Shapiro, Jenessa R

    2012-07-01

    How do Whites approach interracial interactions? We argue that a previously unexamined factor-beliefs about the malleability of racial bias-guides Whites' strategies for difficult interracial interactions. We predicted and found that those who believe racial bias is malleable favor learning-oriented strategies such as taking the other person's perspective and trying to learn why an interaction is challenging, whereas those who believe racial bias is fixed favor performance-oriented strategies such as overcompensating in the interaction and trying to end the interaction as quickly as possible. Four studies support these predictions. Whether measured (Studies 1, 3, and 4) or manipulated (Study 2), beliefs that racial bias is fixed versus malleable yielded these divergent strategies for difficult interracial interactions. Furthermore, beliefs about the malleability of racial bias are distinct from related constructs (e.g., prejudice and motivations to respond without prejudice; Studies 1, 3, and 4) and influence self-reported (Studies 1-3) and actual (Study 4) strategies in imagined (Studies 1-2) and real (Studies 3-4) interracial interactions. Together, these findings demonstrate that beliefs about the malleability of racial bias influence Whites' approaches to and strategies within interracial interactions. PsycINFO Database Record (c) 2012 APA, all rights reserved

  4. False positive reduction in protein-protein interaction predictions using gene ontology annotations

    Directory of Open Access Journals (Sweden)

    Lin Yen-Han

    2007-07-01

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

  5. Tools for Trustworthy Autonomy: Robust Predictions, Intuitive Control, and Optimized Interaction

    OpenAIRE

    Driggs Campbell, Katherine Rose

    2017-01-01

    In the near future, robotics will impact nearly every aspect of life. Yet for technology to smoothly integrate into society, we need interactive systems to be well modeled and predictable; have robust decision making and control; and be trustworthy to improve cooperation and interaction. To achieve these goals, we propose taking a human-centered approach to ease the transition into human-dominated fields. In this work, our modeling methods and control schemes are validated through user stu...

  6. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug

  7. Analysis of clinical drug-drug interaction data to predict uncharacterized interaction magnitudes between antiretroviral drugs and co-medications.

    Science.gov (United States)

    Stader, Felix; Kinvig, Hannah; Battegay, Manuel; Khoo, Saye; Owen, Andrew; Siccardi, Marco; Marzolini, Catia

    2018-04-23

    Despite their high potential for drug-drug-interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required.We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes such as CYP3A and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers and inhibitors of CYP3A, to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs.The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and co-medications were successfully quantified meaning 53%, 85% and 98% of the predictions were within 1.25-fold (0.80 - 1.25), 1.5-fold (0.66 - 1.48) and 2-fold (0.66 - 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or conversely minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs.The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs being particularly relevant in a HIV-setting giving the treatments complexity, high DDI risk and limited guidance on the management of DDIs. Copyright © 2018 American Society for Microbiology.

  8. Efficient prediction of human protein-protein interactions at a global scale.

    Science.gov (United States)

    Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

    2014-12-10

    Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

  9. LCF life prediction for waspaloy in the creep-fatigue interaction regime

    International Nuclear Information System (INIS)

    Yeom, Jong Taek; Park, Nho Kwang

    2001-01-01

    This paper describes the empirical rule of strain rate modified linear accumulation of creep damage(SRM rule) for Low-Cycle Fatigue(LCF) life prediction of Waspaloy in the creep-fatigue interaction regime and Chaboche type unified viscoplastic model predicting the stress-strain response in various cyclic loading conditions. The comparison of the experimental data and the predictions for strain controlled LCF tests carried out for various strain ranges at 600 .deg. C and 650 .deg. C was made. Chaboche type unified viscoplastic model described efficiently the inelastic deformation behavior during LCF tests. Crack-initiation lifting method to predict the material life was investigated with Strain Rate Modification(SRM) rule. The application of SRM rule to LCF tests on Waspaloy indicated a good agreement between measured and predicted cycles to failure

  10. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    Science.gov (United States)

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  11. Contextual Predictive Factors of Child Sexual Abuse: The Role of Parent-Child Interaction

    Science.gov (United States)

    Ramirez, Clemencia; Pinzon-Rondon, Angela Maria; Botero, Juan Carlos

    2011-01-01

    Objectives: To determine the prevalence of child sexual abuse in the Colombian coasts, as well as to assess the role of parent-child interactions on its occurrence and to identify factors from different environmental levels that predict it. Methods: This cross-sectional study explores the results of 1,089 household interviews responded by mothers.…

  12. PREDICTION OF THE MIXING ENTHALPIES OF BINARY LIQUID ALLOYS BY MOLECULAR INTERACTION VOLUME MODEL

    Institute of Scientific and Technical Information of China (English)

    H.W.Yang; D.P.Tao; Z.H.Zhou

    2008-01-01

    The mixing enthalpies of 23 binary liquid alloys are calculated by molecular interaction volume model (MIVM), which is a two-parameter model with the partial molar infinite dilute mixing enthalpies. The predicted values are in agreement with the experimental data and then indicate that the model is reliable and convenient.

  13. Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

    Science.gov (United States)

    Domin, Hanna; Zurita-Gutiérrez, Yazmín H.; Scotti, Marco; Buttlar, Jann; Hentschel Humeida, Ute; Fraune, Sebastian

    2018-01-01

    The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community. PMID:29740401

  14. Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

    Directory of Open Access Journals (Sweden)

    Hanna Domin

    2018-04-01

    Full Text Available The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.

  15. Observation of shadowing of neutrino- and antineutrino-nucleus interactions and comparison with PCAC predictions

    International Nuclear Information System (INIS)

    Allport, P.P.; Radojicic, D.; Guy, J.; Venus, W.; Cooper-Sarkar, A.M.; Aderholz, M.; Schmitz, N.; Wittek, W.; Berggren, M.; Jones, G.T.; O'Neale, S.; Varvell, K.; Marage, P.; Mobayyen, M.; Morrison, D.R.O.; Parker, M.A.; Wachsmuth, H.; Neveu, M.; Simopoulou, E.; Vayaki, A.; Apeldoorn, G. van

    1989-01-01

    Comparing the kinematical distributions of events obtained on neon and deuterium targets in similar experimental conditions reveals a reduction of the neutrino and antineutrino charged current cross section per nucleon in neon at low Q 2 . The effect, interpreted as due to geometric shadowing of the weak propagator in interactions of neutrinos and antineutrinos with nuclei, agrees well with predictions derived from PCAC. (orig.)

  16. Observation of shadowing of neutrino- and antineutrino-nucleus interactions and comparison with PCAC predictions

    Science.gov (United States)

    Allport, P. P.; Erriquez, O.; Guy, J.; Venus, W.; Aderholz, M.; Berggren, M.; Bullock, F. W.; Calicchio, M.; Coghen, T.; Cooper-Sarkar, A. M.; Jones, G. T.; Marage, P.; Mobayyen, M.; Morrison, D. R. O.; Neveu, M.; Parker, M. A.; Radojicic, D.; Sansum, R. A.; Saitta, B.; Schmitz, N.; Simopoulou, E.; O'neale, S.; Van Apeldoorn, G.; Varvell, K.; Vayaki, A.; Wachsmuth, H.; Wittek, W.; BEBC WA59 Collaboration

    1989-12-01

    Comparing the kinematical distributions of events obtained on neon and deuterium targets in similar experimental conditions reveals a reduction of the neutrino and antineutrino charged current cross section per nucleon in neon at low Q2. The effect, interpreted as due to geometric shadowing of the weak propagator in interactions of neutrinos and antineutrinos with nuclei, agrees well with predictions derived from PCAC.

  17. Family Conflict Interacts with Genetic Liability in Predicting Childhood and Adolescent Depression

    Science.gov (United States)

    Rice, Frances; Harold, Gordon T.; Shelton, Katherine H.; Thapar, Anita

    2006-01-01

    Objective: To test for gene-environment interaction with depressive symptoms and family conflict. Specifically, to first examine whether the influence of family conflict in predicting depressive symptoms is increased in individuals at genetic risk of depression. Second, to test whether the genetic component of variance in depressive symptoms…

  18. From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

    Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

  19. Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

    Directory of Open Access Journals (Sweden)

    Tomasz Dzida

    2017-09-01

    Full Text Available We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα, RNA polymerase (Pol II and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

  20. Clustering gene expression data based on predicted differential effects of GV interaction.

    Science.gov (United States)

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

    2005-02-01

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

  1. Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Donald Adjeroh

    2018-03-01

    Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

  2. Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery

  3. How and when predictability interacts with accentuation in temporally selective attention during speech comprehension.

    Science.gov (United States)

    Li, Xiaoqing; Lu, Yong; Zhao, Haiyan

    2014-11-01

    The present study used EEG to investigate how and when top-down prediction interacts with bottom-up acoustic signals in temporally selective attention during speech comprehension. Mandarin Chinese spoken sentences were used as stimuli. We systematically manipulated the predictability and de/accentuation of the critical words in the sentence context. Meanwhile, a linguistic attention probe 'ba' was presented concurrently with the critical words or not. The results showed that, first, words with a linguistic attention probe elicited a larger N1 than those without a probe. The latency of this N1 effect was shortened for accented or lowly predictable words, indicating more attentional resources allocated to these words. Importantly, prediction and accentuation showed a complementary interplay on the latency of this N1 effect, demonstrating that when the words had already attracted attention due to low predictability or due to the presence of pitch accent, the other factor did not modulate attention allocation anymore. Second, relative to the lowly predictable words, the highly predictable words elicited a reduced N400 and enhanced gamma-band power increases, especially under the accented conditions; moreover, under the accented conditions, shorter N1 peak-latency was found to correlate with larger gamma-band power enhancement, which indicates that a close relationship might exist between early selective attention and later semantic integration. Finally, the interaction between top-down selective attention (driven by prediction) and bottom-up selective attention (driven by accentuation) occurred before lexical-semantic processing, namely before the N400 effect evoked by predictability, which was discussed with regard to the language comprehension models. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Social interaction anxiety and personality traits predicting engagement in health risk sexual behaviors.

    Science.gov (United States)

    Rahm-Knigge, Ryan L; Prince, Mark A; Conner, Bradley T

    2018-06-01

    Individuals with social interaction anxiety, a facet of social anxiety disorder, withdraw from or avoid social encounters and generally avoid risks. However, a subset engages in health risk sexual behavior (HRSB). Because sensation seeking, emotion dysregulation, and impulsivity predict engagement in HRSB among adolescents and young adults, the present study hypothesized that latent classes of social interaction anxiety and these personality traits would differentially predict likelihood of engagement in HRSB. Finite mixture modeling was used to discern four classes: two low social interaction anxiety classes distinguished by facets of emotion dysregulation, positive urgency, and negative urgency (Low SIAS High Urgency and Low SIAS Low Urgency) and two high social interaction anxiety classes distinguished by positive urgency, negative urgency, risk seeking, and facets of emotion dysregulation (High SIAS High Urgency and High SIAS Low Urgency). HRSB were entered into the model as auxiliary distal outcomes. Of importance to this study were findings that the High SIAS High Urgency class was more likely to engage in most identified HRSB than the High SIAS Low Urgency class. This study extends previous findings on the heterogeneity of social interaction anxiety by identifying the effects of social interaction anxiety and personality on engagement in HRSB. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

    Science.gov (United States)

    Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

    2017-01-01

    Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  6. A domain-based approach to predict protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Resat Haluk

    2007-06-01

    Full Text Available Abstract Background Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed

  7. Shock loading predictions from application of indicial theory to shock-turbulence interactions

    Science.gov (United States)

    Keefe, Laurence R.; Nixon, David

    1991-01-01

    A sequence of steps that permits prediction of some of the characteristics of the pressure field beneath a fluctuating shock wave from knowledge of the oncoming turbulent boundary layer is presented. The theory first predicts the power spectrum and pdf of the position and velocity of the shock wave, which are then used to obtain the shock frequency distribution, and the pdf of the pressure field, as a function of position within the interaction region. To test the validity of the crucial assumption of linearity, the indicial response of a normal shock is calculated from numerical simulation. This indicial response, after being fit by a simple relaxation model, is used to predict the shock position and velocity spectra, along with the shock passage frequency distribution. The low frequency portion of the shock spectra, where most of the energy is concentrated, is satisfactorily predicted by this method.

  8. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  9. Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

    Directory of Open Access Journals (Sweden)

    Suyu Mei

    Full Text Available Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM, where support vector machine (SVM is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

  10. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

    Science.gov (United States)

    Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

    2015-01-01

    For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  11. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

    Science.gov (United States)

    Kundu, Kousik; Backofen, Rolf

    2017-01-01

    Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

  13. GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

    Science.gov (United States)

    Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

    2018-03-01

    MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

  14. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. The Density Functional Theory of Flies: Predicting distributions of interacting active organisms

    Science.gov (United States)

    Kinkhabwala, Yunus; Valderrama, Juan; Cohen, Itai; Arias, Tomas

    On October 2nd, 2016, 52 people were crushed in a stampede when a crowd panicked at a religious gathering in Ethiopia. The ability to predict the state of a crowd and whether it is susceptible to such transitions could help prevent such catastrophes. While current techniques such as agent based models can predict transitions in emergent behaviors of crowds, the assumptions used to describe the agents are often ad hoc and the simulations are computationally expensive making their application to real-time crowd prediction challenging. Here, we pursue an orthogonal approach and ask whether a reduced set of variables, such as the local densities, are sufficient to describe the state of a crowd. Inspired by the theoretical framework of Density Functional Theory, we have developed a system that uses only measurements of local densities to extract two independent crowd behavior functions: (1) preferences for locations and (2) interactions between individuals. With these two functions, we have accurately predicted how a model system of walking Drosophila melanogaster distributes itself in an arbitrary 2D environment. In addition, this density-based approach measures properties of the crowd from only observations of the crowd itself without any knowledge of the detailed interactions and thus it can make predictions about the resulting distributions of these flies in arbitrary environments, in real-time. This research was supported in part by ARO W911NF-16-1-0433.

  16. Computational prediction of drug-drug interactions based on drugs functional similarities.

    Science.gov (United States)

    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  17. Young adults' internet addiction: Prediction by the interaction of parental marital conflict and respiratory sinus arrhythmia.

    Science.gov (United States)

    Zhang, Hui; Spinrad, Tracy L; Eisenberg, Nancy; Luo, Yun; Wang, Zhenhong

    2017-10-01

    The aim of the current study was to address the potential moderating roles of respiratory sinus arrhythmia (RSA; baseline and suppression) and participant sex in the relation between parents' marital conflict and young adults' internet addiction. Participants included 105 (65 men) Chinese young adults who reported on their internet addiction and their parents' marital conflict. Marital conflict interacted with RSA suppression to predict internet addiction. Specifically, high RSA suppression was associated with low internet addiction, regardless of parental marital conflict; however, for participants with low RSA suppression, a positive relation between marital conflict and internet addiction was found. Internet addiction also was predicted by a significant three-way interaction among baseline RSA, marital conflict, and participant sex. Specifically, for men, marital conflict positively predicted internet addiction under conditions of low (but not high) baseline RSA. For women, marital conflict positively predicted internet addiction under conditions of high (but not low) baseline RSA. Findings highlight the importance of simultaneous consideration of physiological factors, in conjunction with family factors, in the prediction of young adults' internet addiction. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.

    Science.gov (United States)

    Panwar, Bharat; Gupta, Sudheer; Raghava, Gajendra P S

    2013-02-07

    The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0

  19. Computational Approaches for Prediction of Pathogen-Host Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Esmaeil eNourani

    2015-02-01

    Full Text Available Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key part of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. This has motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multi task learning methods are preferred. Here, we present an overview of these computational approaches for PHI prediction, discussing their weakness and abilities, with future directions.

  20. Rotor Wake/Stator Interaction Noise Prediction Code Technical Documentation and User's Manual

    Science.gov (United States)

    Topol, David A.; Mathews, Douglas C.

    2010-01-01

    This report documents the improvements and enhancements made by Pratt & Whitney to two NASA programs which together will calculate noise from a rotor wake/stator interaction. The code is a combination of subroutines from two NASA programs with many new features added by Pratt & Whitney. To do a calculation V072 first uses a semi-empirical wake prediction to calculate the rotor wake characteristics at the stator leading edge. Results from the wake model are then automatically input into a rotor wake/stator interaction analytical noise prediction routine which calculates inlet aft sound power levels for the blade-passage-frequency tones and their harmonics, along with the complex radial mode amplitudes. The code allows for a noise calculation to be performed for a compressor rotor wake/stator interaction, a fan wake/FEGV interaction, or a fan wake/core stator interaction. This report is split into two parts, the first part discusses the technical documentation of the program as improved by Pratt & Whitney. The second part is a user's manual which describes how input files are created and how the code is run.

  1. Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

    Science.gov (United States)

    Staniczenko, Phillip P A; Sivasubramaniam, Prabu; Suttle, K Blake; Pearson, Richard G

    2017-06-01

    Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  2. Prediction of surgical view of neurovascular decompression using interactive computer graphics.

    Science.gov (United States)

    Kin, Taichi; Oyama, Hiroshi; Kamada, Kyousuke; Aoki, Shigeki; Ohtomo, Kuni; Saito, Nobuhito

    2009-07-01

    To assess the value of an interactive visualization method for detecting the offending vessels in neurovascular compression syndrome in patients with facial spasm and trigeminal neuralgia. Computer graphics models are created by fusion of fast imaging employing steady-state acquisition and magnetic resonance angiography. High-resolution magnetic resonance angiography and fast imaging employing steady-state acquisition were performed preoperatively in 17 patients with neurovascular compression syndromes (facial spasm, n = 10; trigeminal neuralgia, n = 7) using a 3.0-T magnetic resonance imaging scanner. Computer graphics models were created with computer software and observed interactively for detection of offending vessels by rotation, enlargement, reduction, and retraction on a graphic workstation. Two-dimensional images were reviewed by 2 radiologists blinded to the clinical details, and 2 neurosurgeons predicted the offending vessel with the interactive visualization method before surgery. Predictions from the 2 imaging approaches were compared with surgical findings. The vessels identified during surgery were assumed to be the true offending vessels. Offending vessels were identified correctly in 16 of 17 patients (94%) using the interactive visualization method and in 10 of 17 patients using 2-dimensional images. These data demonstrated a significant difference (P = 0.015 by Fisher's exact method). The interactive visualization method data corresponded well with surgical findings (surgical field, offending vessels, and nerves). Virtual reality 3-dimensional computer graphics using fusion magnetic resonance angiography and fast imaging employing steady-state acquisition may be helpful for preoperative simulation.

  3. 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.

  4. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

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

  5. Nucleon-nucleon interaction with quark exchanges and prediction to colour van der Waals potential

    International Nuclear Information System (INIS)

    Osman, A.

    1985-11-01

    The nucleon-nucleon interaction is considered by including the colour nucleon clusters. The nucleon-nucleon system is treated as a six-quark system. The obtained local potentials reduce the short-range repulsion. The resulted nucleon-nucleon potential by using a quark-quark potential well agrees with the central-force potentials. The phase shifts calculated by using these local potentials are in good agreement with those obtained from other methods. Introducing the quark-quark potential in the nucleon-nucleon interaction, leads to a colour van der Waals potential very strong compared with that predicted by experiments. (author)

  6. Toward a community ecology of landscapes: predicting multiple predator-prey interactions across geographic space.

    Science.gov (United States)

    Schmitz, Oswald J; Miller, Jennifer R B; Trainor, Anne M; Abrahms, Briana

    2017-09-01

    Community ecology was traditionally an integrative science devoted to studying interactions between species and their abiotic environments in order to predict species' geographic distributions and abundances. Yet for philosophical and methodological reasons, it has become divided into two enterprises: one devoted to local experimentation on species interactions to predict community dynamics; the other devoted to statistical analyses of abiotic and biotic information to describe geographic distribution. Our goal here is to instigate thinking about ways to reconnect the two enterprises and thereby return to a tradition to do integrative science. We focus specifically on the community ecology of predators and prey, which is ripe for integration. This is because there is active, simultaneous interest in experimentally resolving the nature and strength of predator-prey interactions as well as explaining patterns across landscapes and seascapes. We begin by describing a conceptual theory rooted in classical analyses of non-spatial food web modules used to predict species interactions. We show how such modules can be extended to consideration of spatial context using the concept of habitat domain. Habitat domain describes the spatial extent of habitat space that predators and prey use while foraging, which differs from home range, the spatial extent used by an animal to meet all of its daily needs. This conceptual theory can be used to predict how different spatial relations of predators and prey could lead to different emergent multiple predator-prey interactions such as whether predator consumptive or non-consumptive effects should dominate, and whether intraguild predation, predator interference or predator complementarity are expected. We then review the literature on studies of large predator-prey interactions that make conclusions about the nature of multiple predator-prey interactions. This analysis reveals that while many studies provide sufficient information

  7. Identification of residue pairing in interacting β-strands from a predicted residue contact map.

    Science.gov (United States)

    Mao, Wenzhi; Wang, Tong; Zhang, Wenxuan; Gong, Haipeng

    2018-04-19

    Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Our algorithm RDb 2 C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb 2 C. Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C .

  8. Infant attachment security and early childhood behavioral inhibition interact to predict adolescent social anxiety symptoms.

    Science.gov (United States)

    Lewis-Morrarty, Erin; Degnan, Kathryn A; Chronis-Tuscano, Andrea; Pine, Daniel S; Henderson, Heather A; Fox, Nathan A

    2015-01-01

    Insecure attachment and behavioral inhibition (BI) increase risk for internalizing problems, but few longitudinal studies have examined their interaction in predicting adolescent anxiety. This study included 165 adolescents (ages 14-17 years) selected based on their reactivity to novelty at 4 months. Infant attachment was assessed with the Strange Situation. Multimethod BI assessments were conducted across childhood. Adolescents and their parents independently reported on anxiety. The interaction of attachment and BI significantly predicted adolescent anxiety symptoms, such that BI and anxiety were only associated among adolescents with histories of insecure attachment. Exploratory analyses revealed that this effect was driven by insecure-resistant attachment and that the association between BI and social anxiety was significant only for insecure males. Clinical implications are discussed. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

  9. Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

    Science.gov (United States)

    Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

    2016-01-01

    The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

  10. Improving LMA predictions with non-standard interactions: neutrino decay in solar matter?

    CERN Document Server

    Das, C R

    2010-01-01

    It has been known for some time that the well established LMA solution to the observed solar neutrino deficit fails to predict a flat energy spectrum for SuperKamiokande as opposed to what the data indicates. It also leads to a Chlorine rate which appears to be too high as compared to the data. We investigate the possible solution to these inconsistencies with non standard neutrino interactions, assuming that they come as extra contributions to the $\

  11. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

    Directory of Open Access Journals (Sweden)

    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  12. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

    Science.gov (United States)

    Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

    2016-01-01

    Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

  13. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  14. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  15. Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

    Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

  16. Prediction of thermodynamic instabilities of protein solutions from simple protein–protein interactions

    International Nuclear Information System (INIS)

    D’Agostino, Tommaso; Solana, José Ramón; Emanuele, Antonio

    2013-01-01

    Highlights: ► We propose a model of effective protein–protein interaction embedding solvent effects. ► A previous square-well model is enhanced by giving to the interaction a free energy character. ► The temperature dependence of the interaction is due to entropic effects of the solvent. ► The validity of the original SW model is extended to entropy driven phase transitions. ► We get good fits for lysozyme and haemoglobin spinodal data taken from literature. - Abstract: Statistical thermodynamics of protein solutions is often studied in terms of simple, microscopic models of particles interacting via pairwise potentials. Such modelling can reproduce the short range structure of protein solutions at equilibrium and predict thermodynamics instabilities of these systems. We introduce a square well model of effective protein–protein interaction that embeds the solvent’s action. We modify an existing model [45] by considering a well depth having an explicit dependence on temperature, i.e. an explicit free energy character, thus encompassing the statistically relevant configurations of solvent molecules around proteins. We choose protein solutions exhibiting demixing upon temperature decrease (lysozyme, enthalpy driven) and upon temperature increase (haemoglobin, entropy driven). We obtain satisfactory fits of spinodal curves for both the two proteins without adding any mean field term, thus extending the validity of the original model. Our results underline the solvent role in modulating or stretching the interaction potential

  17. Motivational state controls the prediction error in Pavlovian appetitive-aversive interactions.

    Science.gov (United States)

    Laurent, Vincent; Balleine, Bernard W; Westbrook, R Frederick

    2018-01-01

    Contemporary theories of learning emphasize the role of a prediction error signal in driving learning, but the nature of this signal remains hotly debated. Here, we used Pavlovian conditioning in rats to investigate whether primary motivational and emotional states interact to control prediction error. We initially generated cues that positively or negatively predicted an appetitive food outcome. We then assessed how these cues modulated aversive conditioning when a novel cue was paired with a foot shock. We found that a positive predictor of food enhances, whereas a negative predictor of that same food impairs, aversive conditioning. Critically, we also showed that the enhancement produced by the positive predictor is removed by reducing the value of its associated food. In contrast, the impairment triggered by the negative predictor remains insensitive to devaluation of its associated food. These findings provide compelling evidence that the motivational value attributed to a predicted food outcome can directly control appetitive-aversive interactions and, therefore, that motivational processes can modulate emotional processes to generate the final error term on which subsequent learning is based. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    Science.gov (United States)

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  19. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    Science.gov (United States)

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Comparative Genomics and Disorder Prediction Identify Biologically Relevant SH3 Protein Interactions.

    Directory of Open Access Journals (Sweden)

    2005-08-01

    Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

  1. Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2005-08-01

    Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

  2. The Cortisol Response to Anticipated Intergroup Interactions Predicts Self-Reported Prejudice

    Science.gov (United States)

    Bijleveld, Erik; Scheepers, Daan; Ellemers, Naomi

    2012-01-01

    Objectives While prejudice has often been shown to be rooted in experiences of threat, the biological underpinnings of this threat–prejudice association have received less research attention. The present experiment aims to test whether activations of the hypothalamus-pituitary-adrenal (HPA) axis, due to anticipated interactions with out-group members, predict self-reported prejudice. Moreover, we explore potential moderators of this relationship (i.e., interpersonal similarity; subtle vs. blatant prejudice). Methodology/Principal findings Participants anticipated an interaction with an out-group member who was similar or dissimilar to the self. To index HPA activation, cortisol responses to this event were measured. Then, subtle and blatant prejudices were measured via questionnaires. Findings indicated that only when people anticipated an interaction with an out-group member who was dissimilar to the self, their cortisol response to this event significantly predicted subtle (r = .50) and blatant (r = .53) prejudice. Conclusions These findings indicate that prejudicial attitudes are linked to HPA-axis activity. Furthermore, when intergroup interactions are interpreted to be about individuals (and not so much about groups), experienced threat (or its biological substrate) is less likely to relate to prejudice. This conclusion is discussed in terms of recent insights from social neuroscience. PMID:22442709

  3. The cortisol response to anticipated intergroup interactions predicts self-reported prejudice.

    Science.gov (United States)

    Bijleveld, Erik; Scheepers, Daan; Ellemers, Naomi

    2012-01-01

    While prejudice has often been shown to be rooted in experiences of threat, the biological underpinnings of this threat-prejudice association have received less research attention. The present experiment aims to test whether activations of the hypothalamus-pituitary-adrenal (HPA) axis, due to anticipated interactions with out-group members, predict self-reported prejudice. Moreover, we explore potential moderators of this relationship (i.e., interpersonal similarity; subtle vs. blatant prejudice). Participants anticipated an interaction with an out-group member who was similar or dissimilar to the self. To index HPA activation, cortisol responses to this event were measured. Then, subtle and blatant prejudices were measured via questionnaires. Findings indicated that only when people anticipated an interaction with an out-group member who was dissimilar to the self, their cortisol response to this event significantly predicted subtle (r = .50) and blatant (r = .53) prejudice. These findings indicate that prejudicial attitudes are linked to HPA-axis activity. Furthermore, when intergroup interactions are interpreted to be about individuals (and not so much about groups), experienced threat (or its biological substrate) is less likely to relate to prejudice. This conclusion is discussed in terms of recent insights from social neuroscience.

  4. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches

    KAUST Repository

    Olayan, Rawan S.

    2017-12-01

    Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient strategy for systematic screening of a large number of drugs in the attempt to identify new DTIs at low cost and with reasonable accuracy. This necessitates development of accurate computational methods that can help focus on the follow-up experimental validation on a smaller number of highly likely targets for a drug. Although many methods have been proposed for computational DTI prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs. In this report, first, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithm-centric perspectives. The aim is to provide a comprehensive review of computational methods for identifying DTIs, which could help in constructing more reliable methods. Then, we present DDR, an efficient method to predict the existence of DTIs. DDR achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify 5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test setups, and using different performance measures, we show that DDR-FE achieves extremely good performance. Using blind test data, we verified as correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.

  5. Simple knowledge-based descriptors to predict protein-ligand interactions. Methodology and validation

    Science.gov (United States)

    Nissink, J. Willem M.; Verdonk, Marcel L.; Klebe, Gerhard

    2000-11-01

    A new type of shape descriptor is proposed to describe the spatial orientation for non-covalent interactions. It is built from simple, anisotropic Gaussian contributions that are parameterised by 10 adjustable values. The descriptors have been used to fit propensity distributions derived from scatter data stored in the IsoStar database. This database holds composite pictures of possible interaction geometries between a common central group and various interacting moieties, as extracted from small-molecule crystal structures. These distributions can be related to probabilities for the occurrence of certain interaction geometries among different functional groups. A fitting procedure is described that generates the descriptors in a fully automated way. For this purpose, we apply a similarity index that is tailored to the problem, the Split Hodgkin Index. It accounts for the similarity in regions of either high or low propensity in a separate way. Although dependent on the division into these two subregions, the index is robust and performs better than the regular Hodgkin index. The reliability and coverage of the fitted descriptors was assessed using SuperStar. SuperStar usually operates on the raw IsoStar data to calculate propensity distributions, e.g., for a binding site in a protein. For our purpose we modified the code to have it operate on our descriptors instead. This resulted in a substantial reduction in calculation time (factor of five to eight) compared to the original implementation. A validation procedure was performed on a set of 130 protein-ligand complexes, using four representative interacting probes to map the properties of the various binding sites: ammonium nitrogen, alcohol oxygen, carbonyl oxygen, and methyl carbon. The predicted `hot spots' for the binding of these probes were compared to the actual arrangement of ligand atoms in experimentally determined protein-ligand complexes. Results indicate that the version of SuperStar that applies to

  6. Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Chien-Hung Huang

    2015-01-01

    Full Text Available Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues’s method by employing the protein-protein interaction (PPI data, domain-domain interaction (DDI data, weighted domain frequency score (DFS, and cancer linker degree (CLD data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I using individual algorithm, (II combining algorithms, and (III combining the same classification types of algorithms. When compared with Aragues’s method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

  7. An AP endonuclease 1-DNA polymerase beta complex: theoretical prediction of interacting surfaces.

    Directory of Open Access Journals (Sweden)

    Alexej Abyzov

    2008-04-01

    Full Text Available Abasic (AP sites in DNA arise through both endogenous and exogenous mechanisms. Since AP sites can prevent replication and transcription, the cell contains systems for their identification and repair. AP endonuclease (APEX1 cleaves the phosphodiester backbone 5' to the AP site. The cleavage, a key step in the base excision repair pathway, is followed by nucleotide insertion and removal of the downstream deoxyribose moiety, performed most often by DNA polymerase beta (pol-beta. While yeast two-hybrid studies and electrophoretic mobility shift assays provide evidence for interaction of APEX1 and pol-beta, the specifics remain obscure. We describe a theoretical study designed to predict detailed interacting surfaces between APEX1 and pol-beta based on published co-crystal structures of each enzyme bound to DNA. Several potentially interacting complexes were identified by sliding the protein molecules along DNA: two with pol-beta located downstream of APEX1 (3' to the damaged site and three with pol-beta located upstream of APEX1 (5' to the damaged site. Molecular dynamics (MD simulations, ensuring geometrical complementarity of interfaces, enabled us to predict interacting residues and calculate binding energies, which in two cases were sufficient (approximately -10.0 kcal/mol to form a stable complex and in one case a weakly interacting complex. Analysis of interface behavior during MD simulation and visual inspection of interfaces allowed us to conclude that complexes with pol-beta at the 3'-side of APEX1 are those most likely to occur in vivo. Additional multiple sequence analyses of APEX1 and pol-beta in related organisms identified a set of correlated mutations of specific residues at the predicted interfaces. Based on these results, we propose that pol-beta in the open or closed conformation interacts and makes a stable interface with APEX1 bound to a cleaved abasic site on the 3' side. The method described here can be used for analysis in

  8. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

    Science.gov (United States)

    Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

    2017-01-01

    Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

  9. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

    Science.gov (United States)

    Jelínek, Jan; Škoda, Petr; Hoksza, David

    2017-12-06

    Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

  10. A discriminatory function for prediction of protein-DNA interactions based on alpha shape modeling.

    Science.gov (United States)

    Zhou, Weiqiang; Yan, Hong

    2010-10-15

    Protein-DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein-DNA complex are becoming available, the surface characteristics of the complex become an important research topic. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein-DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of -8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of -7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein-DNA interactions. The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm kenandzhou@hotmail.com.

  11. Scoring protein relationships in functional interaction networks predicted from sequence data.

    Directory of Open Access Journals (Sweden)

    Gaston K Mazandu

    Full Text Available UNLABELLED: The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. AVAILABILITY: Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.

  12. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

    Directory of Open Access Journals (Sweden)

    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  13. Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

    Directory of Open Access Journals (Sweden)

    C. Ruben Vosmeer

    2014-01-01

    Full Text Available Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.

  14. An automated decision-tree approach to predicting protein interaction hot spots.

    Science.gov (United States)

    Darnell, Steven J; Page, David; Mitchell, Julie C

    2007-09-01

    Protein-protein interactions can be altered by mutating one or more "hot spots," the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta-Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta-Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface. 2007 Wiley-Liss, Inc.

  15. Modeling plant interspecific interactions from experiments with perennial crop mixtures to predict optimal combinations.

    Science.gov (United States)

    Halty, Virginia; Valdés, Matías; Tejera, Mauricio; Picasso, Valentín; Fort, Hugo

    2017-12-01

    The contribution of plant species richness to productivity and ecosystem functioning is a longstanding issue in ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modeling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modeled communities of perennial crop mixtures by using a generalized Lotka-Volterra model, i.e., a model such that the interspecific interactions are more general than purely competitive. We estimated model parameters -carrying capacities and interaction coefficients- from, respectively, the observed biomass of monocultures and bicultures measured in a large diversity experiment of seven perennial forage species in Iowa, United States. The sign and absolute value of the interaction coefficients showed that the biological interactions between species pairs included amensalism, competition, and parasitism (asymmetric positive-negative interaction), with various degrees of intensity. We tested the model fit by simulating the combinations of more than two species and comparing them with the polycultures experimental data. Overall, theoretical predictions are in good agreement with the experiments. Using this model, we also simulated species combinations that were not sown. From all possible mixtures (sown and not sown) we identified which are the most productive species combinations. Our results demonstrate that a combination of experiments and modeling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. © 2017 by the Ecological Society of America.

  16. Predictability in the Epidemic-Type Aftershock Sequence model of interacting triggered seismicity

    Science.gov (United States)

    Helmstetter, AgnèS.; Sornette, Didier

    2003-10-01

    As part of an effort to develop a systematic methodology for earthquake forecasting, we use a simple model of seismicity on the basis of interacting events which may trigger a cascade of earthquakes, known as the Epidemic-Type Aftershock Sequence model (ETAS). The ETAS model is constructed on a bare (unrenormalized) Omori law, the Gutenberg-Richter law, and the idea that large events trigger more numerous aftershocks. For simplicity, we do not use the information on the spatial location of earthquakes and work only in the time domain. We demonstrate the essential role played by the cascade of triggered seismicity in controlling the rate of aftershock decay as well as the overall level of seismicity in the presence of a constant external seismicity source. We offer an analytical approach to account for the yet unobserved triggered seismicity adapted to the problem of forecasting future seismic rates at varying horizons from the present. Tests presented on synthetic catalogs validate strongly the importance of taking into account all the cascades of still unobserved triggered events in order to predict correctly the future level of seismicity beyond a few minutes. We find a strong predictability if one accepts to predict only a small fraction of the large-magnitude targets. Specifically, we find a prediction gain (defined as the ratio of the fraction of predicted events over the fraction of time in alarms) equal to 21 for a fraction of alarm of 1%, a target magnitude M ≥ 6, an update time of 0.5 days between two predictions, and for realistic parameters of the ETAS model. However, the probability gains degrade fast when one attempts to predict a larger fraction of the targets. This is because a significant fraction of events remain uncorrelated from past seismicity. This delineates the fundamental limits underlying forecasting skills, stemming from an intrinsic stochastic component in these interacting triggered seismicity models. Quantitatively, the fundamental

  17. Finding low-conductance sets with dense interactions (FLCD) for better protein complex prediction.

    Science.gov (United States)

    Wang, Yijie; Qian, Xiaoning

    2017-03-14

    Intuitively, proteins in the same protein complexes should highly interact with each other but rarely interact with the other proteins in protein-protein interaction (PPI) networks. Surprisingly, many existing computational algorithms do not directly detect protein complexes based on both of these topological properties. Most of them, depending on mathematical definitions of either "modularity" or "conductance", have their own limitations: Modularity has the inherent resolution problem ignoring small protein complexes; and conductance characterizes the separability of complexes but fails to capture the interaction density within complexes. In this paper, we propose a two-step algorithm FLCD (Finding Low-Conductance sets with Dense interactions) to predict overlapping protein complexes with the desired topological structure, which is densely connected inside and well separated from the rest of the networks. First, FLCD detects well-separated subnetworks based on approximating a potential low-conductance set through a personalized PageRank vector from a protein and then solving a mixed integer programming (MIP) problem to find the minimum-conductance set within the identified low-conductance set. At the second step, the densely connected parts in those subnetworks are discovered as the protein complexes by solving another MIP problem that aims to find the dense subnetwork in the minimum-conductance set. Experiments on four large-scale yeast PPI networks from different public databases demonstrate that the complexes predicted by FLCD have better correspondence with the yeast protein complex gold standards than other three state-of-the-art algorithms (ClusterONE, LinkComm, and SR-MCL). Additionally, results of FLCD show higher biological relevance with respect to Gene Ontology (GO) terms by GO enrichment analysis.

  18. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.

    Science.gov (United States)

    Lopez-Cruz, Marco; Crossa, Jose; Bonnett, David; Dreisigacker, Susanne; Poland, Jesse; Jannink, Jean-Luc; Singh, Ravi P; Autrique, Enrique; de los Campos, Gustavo

    2015-02-06

    Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are

  19. Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

    Directory of Open Access Journals (Sweden)

    Xiuquan Du

    2015-01-01

    Full Text Available Protein-protein interactions (PPIs play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM and much higher than naive Bayes (NB, neural networks (NN, and k-nearest neighbor (KNN. The result shows that the MSRC is efficient for PPIs prediction.

  20. Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Vikas, E-mail: vikas.kumar@urv.cat [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Barros, Felipe P.J. de [Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles 90089, CA (United States); Schuhmacher, Marta [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier [Hydrogeology Group, Department of Geotechnical Engineering and Geosciences, University Politècnica de Catalunya-BarcelonaTech, Barcelona 08034 (Spain)

    2013-12-15

    Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

  1. Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

    Science.gov (United States)

    Chen, Ruoying; Zhang, Zhiwang; Wu, Di; Zhang, Peng; Zhang, Xinyang; Wang, Yong; Shi, Yong

    2011-01-21

    Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids. Copyright © 2010 Elsevier Ltd. All rights reserved.

  2. Harsh parenting and fearfulness in toddlerhood interact to predict amplitudes of preschool error-related negativity

    Directory of Open Access Journals (Sweden)

    Rebecca J. Brooker

    2014-07-01

    Full Text Available Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children's risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN, an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems.

  3. Harsh parenting and fearfulness in toddlerhood interact to predict amplitudes of preschool error-related negativity.

    Science.gov (United States)

    Brooker, Rebecca J; Buss, Kristin A

    2014-07-01

    Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children's risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. The Interaction between Interoceptive and Action States within a Framework of Predictive Coding

    Science.gov (United States)

    Marshall, Amanda C.; Gentsch, Antje; Schütz-Bosbach, Simone

    2018-01-01

    The notion of predictive coding assumes that perception is an iterative process between prior knowledge and sensory feedback. To date, this perspective has been primarily applied to exteroceptive perception as well as action and its associated phenomenological experiences such as agency. More recently, this predictive, inferential framework has been theoretically extended to interoception. This idea postulates that subjective feeling states are generated by top–down inferences made about internal and external causes of interoceptive afferents. While the processing of motor signals for action control and the emergence of selfhood have been studied extensively, the contributions of interoceptive input and especially the potential interaction of motor and interoceptive signals remain largely unaddressed. Here, we argue for a specific functional relation between motor and interoceptive awareness. Specifically, we implicate interoceptive predictions in the generation of subjective motor-related feeling states. Furthermore, we propose a distinction between reflexive and pre-reflexive modes of agentic action control and suggest that interoceptive input may affect each differently. Finally, we advocate the necessity of continuous interoceptive input for conscious forms of agentic action control. We conclude by discussing further research contributions that would allow for a fuller understanding of the interaction between agency and interoceptive awareness. PMID:29515495

  5. Prediction of the Cabibbo angle in the vector model for electroweak interactions

    International Nuclear Information System (INIS)

    Reifler, F.; Morris, R.

    1985-01-01

    In a recent paper we presented a vector model for the electroweak interactions which is similar to the Weinberg--Salam model but differs in the following features. (1) In the vector model all fermion wave functions are bispinors or equivalently isotropic Yang--Mills triplets (as opposed to a state vector composed of a spinor and bispinors in the Weinberg--Salam model). Particles are distinguished by their Higgs fields. (2) The vector model predicts that sin 2 theta/sub W/ = 1/4 , where theta/sub W/ is the Weinberg angle. (3) The vector model accounts for conservation of lepton number, electric charge, and baryon number. (4) In the vector model an antiparticle is characterized by opposite lepton number, electric charge, and baryon number; yet both particles and antiparticles propagate forward in time with positive energies. In this paper we extend the vector theory to include interactions between fermions and the gauge bosons mediating the electroweak force. We model the bosons as Yang--Mills fields with their own Higgs fields. We further propose a specific configuration of Higgs fields for the u,d,s, and c quarks. With these features, the model accounts for electroweak transitions of quarks and leptons and predicts that cos theta/sub C/ = 0.9744, where theta/sub C/ is the Cabibbo angle. We further show that the vector model accounts for the intrinsic parity of particles and antiparticles, and parity violations and CPT invariance for electroweak interactions

  6. Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

    Science.gov (United States)

    Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

    2016-01-01

    Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

  7. Dengue serotype immune-interactions and their consequences for vaccine impact predictions

    Directory of Open Access Journals (Sweden)

    José Lourenço

    2016-09-01

    Full Text Available Dengue is one of the most important and wide-spread viral infections affecting human populations. The last few decades have seen a dramatic increase in the global burden of dengue, with the virus now being endemic or near-endemic in over 100 countries world-wide. A recombinant tetravalent vaccine candidate (CYD-TDV has recently completed Phase III clinical efficacy trials in South East Asia and Latin America and has been licensed for use in several countries. The trial results showed moderate-to-high efficacies in protection against clinical symptoms and hospitalisation but with so far unknown effects on transmission and infections per se. Model-based predictions about the vaccine's short- or long-term impact on the burden of dengue are therefore subject to a considerable degree of uncertainty. Furthermore, different immune interactions between dengue's serotypes have frequently been evoked by modelling studies to underlie dengue's oscillatory dynamics in disease incidence and serotype prevalence. Here we show how model assumptions regarding immune interactions in the form of antibody-dependent enhancement, temporary cross-immunity and the number of infections required to develop full immunity can significantly affect the predicted outcome of a dengue vaccination campaign. Our results thus re-emphasise the important gap in our current knowledge concerning the effects of previous exposure on subsequent dengue infections and further suggest that intervention impact studies should be critically evaluated by their underlying assumptions about serotype immune-interactions.

  8. HitPredict version 4: comprehensive reliability scoring of physical protein?protein interactions from more than 100 species

    OpenAIRE

    L?pez, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein?protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein?protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of p...

  9. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-08-31

    The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

  10. Association with humans and seasonality interact to reverse predictions for animal space use.

    Science.gov (United States)

    Laver, Peter N; Alexander, Kathleen A

    2018-01-01

    Variation in animal space use reflects fitness trade-offs associated with ecological constraints. Associated theories such as the metabolic theory of ecology and the resource dispersion hypothesis generate predictions about what drives variation in animal space use. But, metabolic theory is usually tested in macro-ecological studies and is seldom invoked explicitly in within-species studies. Full evaluation of the resource dispersion hypothesis requires testing in more species. Neither have been evaluated in the context of anthropogenic landscape change. In this study, we used data for banded mongooses ( Mungos mungo ) in northeastern Botswana, along a gradient of association with humans, to test for effects of space use drivers predicted by these theories. We used Bayesian parameter estimation and inference from linear models to test for seasonal differences in space use metrics and to model seasonal effects of space use drivers. Results suggest that space use is strongly associated with variation in the level of overlap that mongoose groups have with humans. Seasonality influences this association, reversing seasonal space use predictions historically-accepted by ecologists. We found support for predictions of the metabolic theory when moderated by seasonality, by association with humans and by their interaction. Space use of mongooses living in association with humans was more concentrated in the dry season than the wet season, when historically-accepted ecological theory predicted more dispersed space use. Resource richness factors such as building density were associated with space use only during the dry season. We found negligible support for predictions of the resource dispersion hypothesis in general or for metabolic theory where seasonality and association with humans were not included. For mongooses living in association with humans, space use was not associated with patch dispersion or group size over both seasons. In our study, living in association

  11. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

    Science.gov (United States)

    Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

    2018-04-11

    Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree

  12. Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

    Directory of Open Access Journals (Sweden)

    Panwar Bharat

    2013-02-01

    Full Text Available Abstract Background The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. Results In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL. It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i vitamin interacting residues (VIRs, (ii vitamin-A interacting residues (VAIRs, (iii vitamin-B interacting residues (VBIRs and (iv pyridoxal-5-phosphate (vitamin B6 interacting residues (PLPIRs have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM features of protein sequences. Finally, we selected best performing SVM modules and

  13. Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

    Directory of Open Access Journals (Sweden)

    Diego Jarquin

    2018-04-01

    Full Text Available Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage data recorded between days 14–71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015 on a soybean nested association mapping population (SoyNAM was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i incomplete field trials (CV2; (ii newly developed lines (CV1; and (iii predicting lines in unobserved environments (CV0. Two different traits were evaluated in this study: yield and days to maturity (DTM. Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27–123%, 27–148%, and 65–165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14–33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season.

  14. Modeling and prediction of human word search behavior in interactive machine translation

    Science.gov (United States)

    Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

    2017-12-01

    As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

  15. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  16. Psychopathic Traits and Moral Disengagement Interact to Predict Bullying and Cyberbullying Among Adolescents.

    Science.gov (United States)

    Orue, Izaskun; Calvete, Esther

    2016-07-01

    The aim of this study was to test a model in which psychopathic traits (callous-unemotional, grandiose-manipulative, and impulsive-irresponsible) and moral disengagement individually and interactively predict two types of bullying (traditional and cyberbullying) in a community sample of adolescents. A total of 765 adolescents (464 girls and 301 boys) completed measures of moral disengagement and psychopathic traits at Time 1, and measures of bullying and cyberbullying at Time 1 and 1 year later, at Time 2. The results showed that callous-unemotional traits predicted both traditional bullying and cyberbullying, grandiose-manipulative and impulsive-irresponsible traits only predicted traditional bullying, and moral disengagement only predicted cyberbullying. Callous-Unemotional Traits × Moral Disengagement and Grandiose-Manipulative × Moral Disengagement were significantly correlated with the residual change in cyberbullying. Callous-unemotional traits were positively related to cyberbullying at high levels of moral disengagement but not when moral disengagement was low. In contrast, grandiose-manipulative traits were positively related to cyberbullying at low levels of moral disengagement but not when moral disengagement was high. These findings have implications for both prevention and intervention. Integrative approaches that promote moral growth are needed, including a deeper understanding of why bullying is morally wrong and ways to stimulate personality traits that counteract psychopathic traits.

  17. Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

    Directory of Open Access Journals (Sweden)

    Sze Sing-Hoi

    2008-07-01

    Full Text Available Abstract Background Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins. Results By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states. Conclusion Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold is available at http://faculty.cs.tamu.edu/shsze/ssfold.

  18. The separate and interactive effects of drinking motives and social anxiety symptoms in predicting drinking outcomes.

    Science.gov (United States)

    Clerkin, Elise M; Barnett, Nancy

    2012-05-01

    Our goal was to test the separate and interactive effects of drinking motives and social anxiety symptoms in predicting drinking-related consumption and problems. Participants (N=730; 59.7% female) were undergraduate college students who completed measures of social anxiety symptoms, drinking motives, alcohol consumption, and drinking problems. Greater social anxiety symptoms were significantly associated with less alcohol consumption, and there was some evidence that greater social anxiety symptoms were also associated with greater alcohol-relevant problems. Significant interactions between social anxiety and motives indicated that a) alcohol use was most pronounced for individuals high in enhancement motives and low in social anxiety symptoms; and b) among participants low in coping motives, drinking problems were greater for individuals high (vs. low) in social anxiety symptoms. More fully identifying the individual difference factors that link social anxiety symptoms with drinking outcomes is important for informing prevention and intervention approaches. Copyright © 2012 Elsevier Ltd. All rights reserved.

  19. Prediction of interactions between viral and host proteins using supervised machine learning methods.

    Directory of Open Access Journals (Sweden)

    Ranjan Kumar Barman

    Full Text Available BACKGROUND: Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs has great implication for therapeutics. METHODS: In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques. RESULTS: Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49% and Random Forest (55.66%. However the specificity of Naïve Bayes was the highest (99.52% as compared with SVM (74% and Random Forest (89.08%. Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively. CONCLUSION: The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV, interacting partners of host

  20. Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

    DEFF Research Database (Denmark)

    Zong, Yi; Böning, Georg Martin; Santos, Rui Mirra

    2016-01-01

    ) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings......When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC...

  1. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Carson M Andorf

    Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  2. Predicting decisions in human social interactions using real-time fMRI and pattern classification.

    Science.gov (United States)

    Hollmann, Maurice; Rieger, Jochem W; Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

    2011-01-01

    Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

  3. FKBP5 and emotional neglect interact to predict individual differences in amygdala reactivity.

    Science.gov (United States)

    White, M G; Bogdan, R; Fisher, P M; Muñoz, K E; Williamson, D E; Hariri, A R

    2012-10-01

    Individual variation in physiological responsiveness to stress mediates risk for mental illness and is influenced by both experiential and genetic factors. Common polymorphisms in the human gene for FK506 binding protein 5 (FKBP5), which is involved in transcriptional regulation of the hypothalamic-pituitary-adrenal (HPA) axis, have been shown to interact with childhood abuse and trauma to predict stress-related psychopathology. In the current study, we examined if such gene-environment interaction effects may be related to variability in the threat-related reactivity of the amygdala, which plays a critical role in mediating physiological and behavioral adaptations to stress including modulation of the HPA axis. To this end, 139 healthy Caucasian youth completed a blood oxygen level-dependent functional magnetic resonance imaging probe of amygdala reactivity and self-report assessments of emotional neglect (EN) and other forms of maltreatment. These individuals were genotyped for 6 FKBP5 polymorphisms (rs7748266, rs1360780, rs9296158, rs3800373, rs9470080 and rs9394309) previously associated with psychopathology and/or HPA axis function. Interactions between each SNP and EN emerged such that risk alleles predicted relatively increased dorsal amygdala reactivity in the context of higher EN, even after correcting for multiple testing. Two different haplotype analyses confirmed this relationship as haplotypes with risk alleles also exhibited increased amygdala reactivity in the context of higher EN. Our results suggest that increased threat-related amygdala reactivity may represent a mechanism linking psychopathology to interactions between common genetic variants affecting HPA axis function and childhood trauma. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

  4. Interactions between risk factors in the prediction of onset of eating disorders: Exploratory hypothesis generating analyses.

    Science.gov (United States)

    Stice, Eric; Desjardins, Christopher D

    2018-06-01

    Because no study has tested for interactions between risk factors in the prediction of future onset of each eating disorder, this exploratory study addressed this lacuna to generate hypotheses to be tested in future confirmatory studies. Data from three prevention trials that targeted young women at high risk for eating disorders due to body dissatisfaction (N = 1271; M age 18.5, SD 4.2) and collected diagnostic interview data over 3-year follow-up were combined to permit sufficient power to predict onset of anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and purging disorder (PD) using classification tree analyses, an analytic technique uniquely suited to detecting interactions. Low BMI was the most potent predictor of AN onset, and body dissatisfaction amplified this relation. Overeating was the most potent predictor of BN onset, and positive expectancies for thinness and body dissatisfaction amplified this relation. Body dissatisfaction was the most potent predictor of BED onset, and overeating, low dieting, and thin-ideal internalization amplified this relation. Dieting was the most potent predictor of PD onset, and negative affect and positive expectancies for thinness amplified this relation. Results provided evidence of amplifying interactions between risk factors suggestive of cumulative risk processes that were distinct for each disorder; future confirmatory studies should test the interactive hypotheses generated by these analyses. If hypotheses are confirmed, results may allow interventionists to target ultra high-risk subpopulations with more intensive prevention programs that are uniquely tailored for each eating disorder, potentially improving the yield of prevention efforts. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives

    Directory of Open Access Journals (Sweden)

    Oriane Dermy

    2017-10-01

    Full Text Available This article describes our open-source software for predicting the intention of a user physically interacting with the humanoid robot iCub. Our goal is to allow the robot to infer the intention of the human partner during collaboration, by predicting the future intended trajectory: this capability is critical to design anticipatory behaviors that are crucial in human–robot collaborative scenarios, such as in co-manipulation, cooperative assembly, or transportation. We propose an approach to endow the iCub with basic capabilities of intention recognition, based on Probabilistic Movement Primitives (ProMPs, a versatile method for representing, generalizing, and reproducing complex motor skills. The robot learns a set of motion primitives from several demonstrations, provided by the human via physical interaction. During training, we model the collaborative scenario using human demonstrations. During the reproduction of the collaborative task, we use the acquired knowledge to recognize the intention of the human partner. Using a few early observations of the state of the robot, we can not only infer the intention of the partner but also complete the movement, even if the user breaks the physical interaction with the robot. We evaluate our approach in simulation and on the real iCub. In simulation, the iCub is driven by the user using the Geomagic Touch haptic device. In the real robot experiment, we directly interact with the iCub by grabbing and manually guiding the robot’s arm. We realize two experiments on the real robot: one with simple reaching trajectories, and one inspired by collaborative object sorting. The software implementing our approach is open source and available on the GitHub platform. In addition, we provide tutorials and videos.

  6. Predicting decisions in human social interactions using real-time fMRI and pattern classification.

    Directory of Open Access Journals (Sweden)

    Maurice Hollmann

    Full Text Available Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

  7. Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

    Science.gov (United States)

    Kundu, Kousik; Costa, Fabrizio; Huber, Michael; Reth, Michael; Backofen, Rolf

    2013-01-01

    Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2

  8. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    Science.gov (United States)

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  9. Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Rezaei, Javad; Hugo, Willy; Gao, Shangzhi; Jin, Jingjing; Fan, Mengyuan; Yong, Chern-Han; Wozniak, Michal; Wong, Limsoon

    2013-01-01

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some

  10. The multidimensional behavioural hypervolumes of two interacting species predict their space use and survival.

    Science.gov (United States)

    Lichtenstein, James L L; Wright, Colin M; McEwen, Brendan; Pinter-Wollman, Noa; Pruitt, Jonathan N

    2017-10-01

    Individual animals differ consistently in their behaviour, thus impacting a wide variety of ecological outcomes. Recent advances in animal personality research have established the ecological importance of the multidimensional behavioural volume occupied by individuals and by multispecies communities. Here, we examine the degree to which the multidimensional behavioural volume of a group predicts the outcome of both intra- and interspecific interactions. In particular, we test the hypothesis that a population of conspecifics will experience low intraspecific competition when the population occupies a large volume in behavioural space. We further hypothesize that populations of interacting species will exhibit greater interspecific competition when one or both species occupy large volumes in behavioural space. We evaluate these hypotheses by studying groups of katydids ( Scudderia nymphs) and froghoppers ( Philaenus spumarius ), which compete for food and space on their shared host plant, Solidago canadensis . We found that individuals in single-species groups of katydids positioned themselves closer to one another, suggesting reduced competition, when groups occupied a large behavioural volume. When both species were placed together, we found that the survival of froghoppers was greatest when both froghoppers and katydids occupied a small volume in behavioural space, particularly at high froghopper densities. These results suggest that groups that occupy large behavioural volumes can have low intraspecific competition but high interspecific competition. Thus, behavioural hypervolumes appear to have ecological consequences at both the level of the population and the community and may help to predict the intensity of competition both within and across species.

  11. Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

    Science.gov (United States)

    Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

    2017-10-11

    Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

  12. Predicting Parent-Child Aggression Risk: Cognitive Factors and Their Interaction With Anger.

    Science.gov (United States)

    Rodriguez, Christina M

    2018-02-01

    Several cognitive elements have previously been proposed to elevate risk for physical child abuse. To predict parent-child aggression risk, the current study evaluated the role of approval of parent-child aggression, perceptions of children as poorly behaved, and discipline attributions. Several dimensions of attributions specifically tied to parents' discipline practices were targeted. In addition, anger experienced during discipline episodes was considered a potential moderator of these cognitive processes. Using a largely multiple-indicator approach, a sample of 110 mothers reported on these cognitive and affective aspects that may occur when disciplining their children as well as responding to measures of parent-child aggression risk. Findings suggest that greater approval of parent-child aggression, negative perceptions of their child's behavior, and discipline attributions independently predicted parent-child aggression risk, with anger significantly interacting with mothers' perception of their child as more poorly behaved to exacerbate their parent-child aggression risk. Of the discipline attribution dimensions evaluated, mothers' sense of external locus of control and believing their child deserved their discipline were related to increase parent-child aggression risk. Future work is encouraged to comprehensively evaluate how cognitive and affective components contribute and interact to increase risk for parent-child aggression.

  13. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

    Science.gov (United States)

    Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen

    2014-03-19

    Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  14. Situational Motivation and Perceived Intensity: Their Interaction in Predicting Changes in Positive Affect from Physical Activity

    Directory of Open Access Journals (Sweden)

    Eva Guérin

    2012-01-01

    Full Text Available There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE] in predicting changes in positive affect following an acute bout of preferred physical activity, namely, running. Fourty-one female runners engaged in a 30-minute self-paced treadmill run in a laboratory context. Situational motivation for running, pre- and post-running positive affect, and RPE were assessed via validated self-report questionnaires. Hierarchical regression analyses revealed a significant interaction effect between RPE and introjection (P<.05 but not between RPE and identified regulation or intrinsic motivation. At low levels of introjection, the influence of RPE on the change in positive affect was considerable, with higher RPE ratings being associated with greater increases in positive affect. The implications of the findings in light of SDT principles as well as the potential contingencies between the regulations and RPE in predicting positive affect among women are discussed.

  15. Predicting Individual Action Switching in Passively Experienced and Continuous Interactive Tasks Using the Fluid Events Model

    Directory of Open Access Journals (Sweden)

    Gabriel A. Radvansky

    2016-01-01

    Full Text Available The Fluid Events Model is aimed at predicting changes in the actions people take on a moment-by-moment basis. In contrast with other research on action selection, this work does not investigate why some course of action was selected, but rather the likelihood of discontinuing the current course of action and selecting another in the near future. This is done using both task-based and experience-based factors. Prior work evaluated this model in the context of trial-by-trial, independent, interactive events, such as choosing how to copy a figure of a line drawing. In this paper, we extend this model to more covert event experiences, such as reading narratives, as well as to continuous interactive events, such as playing a video game. To this end, the model was applied to existing data sets of reading time and event segmentation for written and picture stories. It was also applied to existing data sets of performance in a strategy board game, an aerial combat game, and a first person shooter game in which a participant’s current state was dependent on prior events. The results revealed that the model predicted behavior changes well, taking into account both the theoretically defined structure of the described events, as well as a person’s prior experience. Thus, theories of event cognition can benefit from efforts that take into account not only how events in the world are structured, but also how people experience those events.

  16. Predicting Individual Action Switching in Covert and Continuous Interactive Tasks Using the Fluid Events Model.

    Science.gov (United States)

    Radvansky, Gabriel A; D'Mello, Sidney K; Abbott, Robert G; Bixler, Robert E

    2016-01-01

    The Fluid Events Model is aimed at predicting changes in the actions people take on a moment-by-moment basis. In contrast with other research on action selection, this work does not investigate why some course of action was selected, but rather the likelihood of discontinuing the current course of action and selecting another in the near future. This is done using both task-based and experience-based factors. Prior work evaluated this model in the context of trial-by-trial, independent, interactive events, such as choosing how to copy a figure of a line drawing. In this paper, we extend this model to more covert event experiences, such as reading narratives, as well as to continuous interactive events, such as playing a video game. To this end, the model was applied to existing data sets of reading time and event segmentation for written and picture stories. It was also applied to existing data sets of performance in a strategy board game, an aerial combat game, and a first person shooter game in which a participant's current state was dependent on prior events. The results revealed that the model predicted behavior changes well, taking into account both the theoretically defined structure of the described events, as well as a person's prior experience. Thus, theories of event cognition can benefit from efforts that take into account not only how events in the world are structured, but also how people experience those events.

  17. Situational motivation and perceived intensity: their interaction in predicting changes in positive affect from physical activity.

    Science.gov (United States)

    Guérin, Eva; Fortier, Michelle S

    2012-01-01

    There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE)] in predicting changes in positive affect following an acute bout of preferred physical activity, namely, running. Fourty-one female runners engaged in a 30-minute self-paced treadmill run in a laboratory context. Situational motivation for running, pre- and post-running positive affect, and RPE were assessed via validated self-report questionnaires. Hierarchical regression analyses revealed a significant interaction effect between RPE and introjection (P positive affect was considerable, with higher RPE ratings being associated with greater increases in positive affect. The implications of the findings in light of SDT principles as well as the potential contingencies between the regulations and RPE in predicting positive affect among women are discussed.

  18. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2017-11-01

    Full Text Available Protein-protein interactions (PPIs play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs and a novel local conjoint triad description (LCTD feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

  19. iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

    Directory of Open Access Journals (Sweden)

    Yue-Nong Fan

    2014-03-01

    Full Text Available Nuclear receptors (NRs are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  20. Attentional and motor impulsivity interactively predict 'food addiction' in obese individuals.

    Science.gov (United States)

    Meule, Adrian; de Zwaan, Martina; Müller, Astrid

    2017-01-01

    Impulsivity is a multifaceted construct and constitutes a common risk factor for a range of behaviors associated with poor self-control (e.g., substance use or binge eating). The short form of the Barratt Impulsiveness Scale (BIS-15) measures impulsive behaviors related to attentional (inability to focus attention or concentrate), motor (acting without thinking), and non-planning (lack of future orientation or forethought) impulsivity. Eating-related measures appear to be particularly related to attentional and motor impulsivity and recent findings suggest that interactive effects between these two facets may play a role in eating- and weight-regulation. One-hundred thirty-three obese individuals presenting for bariatric surgery (77.4% female) completed the BIS-15 and the Yale Food Addiction Scale (YFAS) 2.0, which measures addiction-like eating based on the eleven symptoms of substance use disorder outlined in the fifth version of the Diagnostic and Statistical Manual of Mental Disorders. Sixty-three participants (47.4%) were classified as being 'food addicted'. Scores on attentional and motor impulsivity interactively predicted 'food addiction' status: higher attentional impulsivity was associated with a higher likelihood of receiving a YFAS 2.0 diagnosis only at high (+1 SD), but not at low (-1 SD) levels of motor impulsivity. Results support previous findings showing that non-planning impulsivity does not appear to play a role in eating-related self-regulation. Furthermore, this is the first study that shows interactive effects between different impulsivity facets when predicting 'food addiction' in obese individuals. Self-regulatory failure in eating-regulation (e.g., addiction-like overeating) may particularly emerge when both attentional and motor impulsivity levels are elevated. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Separate and interactive contributions of weak inhibitory control and threat sensitivity to prediction of suicide risk.

    Science.gov (United States)

    Venables, Noah C; Sellbom, Martin; Sourander, Andre; Kendler, Kenneth S; Joiner, Thomas E; Drislane, Laura E; Sillanmäki, Lauri; Elonheimo, Henrik; Parkkola, Kai; Multimaki, Petteri; Patrick, Christopher J

    2015-04-30

    Biobehavioral dispositions can serve as valuable referents for biologically oriented research on core processes with relevance to many psychiatric conditions. The present study examined two such dispositional variables-weak response inhibition (or disinhibition; INH-) and threat sensitivity (or fearfulness; THT+)-as predictors of the serious transdiagnostic problem of suicide risk in two samples: male and female outpatients from a U.S. clinic (N=1078), and a population-based male military cohort from Finland (N=3855). INH- and THT+ were operationalized through scores on scale measures of disinhibition and fear/fearlessness, known to be related to DSM-defined clinical conditions and brain biomarkers. Suicide risk was assessed by clinician ratings (clinic sample) and questionnaires (both samples). Across samples and alternative suicide indices, INH- and THT+ each contributed uniquely to prediction of suicide risk-beyond internalizing and externalizing problems in the case of the clinic sample where diagnostic data were available. Further, in both samples, INH- and THT+ interactively predicted suicide risk, with individuals scoring concurrently high on both dispositions exhibiting markedly augmented risk. Findings demonstrate that dispositional constructs of INH- and THT+ are predictive of suicide risk, and hold potential as referents for biological research on suicidal behavior. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  2. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

    Science.gov (United States)

    Xie, Weihong; Yu, Yang

    2017-01-01

    Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

  3. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

    Directory of Open Access Journals (Sweden)

    Fan Liang

    2017-01-01

    Full Text Available Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

  4. Fukushima Daiichi Unit 1 Ex-Vessel Prediction: Core Concrete Interaction

    International Nuclear Information System (INIS)

    Robb, Kevin R; Farmer, Mitchell; Francis, Matthew W

    2015-01-01

    Lower head failure and corium concrete interaction were predicted to occur at Fukushima Daiichi Unit 1 (1F1) by several different system-level code analyses, including MELCOR v2.1 and MAAP5. Although these codes capture a wide range of accident phenomena, they do not contain detailed models for ex-vessel core melt behavior. However, specialized codes exist for analysis of ex-vessel melt spreading (e.g., MELTSPREAD) and long-term debris coolability (e.g., CORQUENCH). On this basis, an analysis was carried out to further evaluate ex-vessel behavior for 1F1 using MELTSPREAD and CORQUENCH. Best-estimate melt pour conditions predicted by MELCOR v2.1 and MAAP5 were used as input. MELTSPREAD was then used to predict the spatially dependent melt conditions and extent of spreading during relocation from the vessel. The results of the MELTSPREAD analysis are reported in a companion paper. This information was used as input for the long-term debris coolability analysis with CORQUENCH.

  5. PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

    Directory of Open Access Journals (Sweden)

    Greenblatt Jack

    2006-07-01

    Full Text Available Abstract Background Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions. Results Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30 and YMR135C (gid8 yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c. The observed interaction was confirmed by tandem affinity purification (TAP tag, verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not

  6. 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

  7. A turbulent mixing Reynolds stress model fitted to match linear interaction analysis predictions

    International Nuclear Information System (INIS)

    Griffond, J; Soulard, O; Souffland, D

    2010-01-01

    To predict the evolution of turbulent mixing zones developing in shock tube experiments with different gases, a turbulence model must be able to reliably evaluate the production due to the shock-turbulence interaction. In the limit of homogeneous weak turbulence, 'linear interaction analysis' (LIA) can be applied. This theory relies on Kovasznay's decomposition and allows the computation of waves transmitted or produced at the shock front. With assumptions about the composition of the upstream turbulent mixture, one can connect the second-order moments downstream from the shock front to those upstream through a transfer matrix, depending on shock strength. The purpose of this work is to provide a turbulence model that matches LIA results for the shock-turbulent mixture interaction. Reynolds stress models (RSMs) with additional equations for the density-velocity correlation and the density variance are considered here. The turbulent states upstream and downstream from the shock front calculated with these models can also be related through a transfer matrix, provided that the numerical implementation is based on a pseudo-pressure formulation. Then, the RSM should be modified in such a way that its transfer matrix matches the LIA one. Using the pseudo-pressure to introduce ad hoc production terms, we are able to obtain a close agreement between LIA and RSM matrices for any shock strength and thus improve the capabilities of the RSM.

  8. Do Core Interpersonal and Affective Traits of PCL-R Psychopathy Interact with Antisocial Behavior and Disinhibition to Predict Violence?

    Science.gov (United States)

    Kennealy, Patrick J.; Skeem, Jennifer L.; Walters, Glenn D.; Camp, Jacqueline

    2010-01-01

    The utility of psychopathy measures in predicting violence is largely explained by their assessment of social deviance (e.g., antisocial behavior; disinhibition). A key question is whether social deviance "interacts" with the core interpersonal-affective traits of psychopathy to predict violence. Do core psychopathic traits multiply the (already…

  9. AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Jiansong Fang

    Full Text Available Alzheimer's disease (AD is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs to delay disease progression. The in silico prediction of chemical-protein interactions (CPI can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB and recursive partitioning (RP algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

  10. Psychosocial safety climate moderates the job demand-resource interaction in predicting workgroup distress.

    Science.gov (United States)

    Dollard, Maureen F; Tuckey, Michelle R; Dormann, Christian

    2012-03-01

    Psychosocial safety climate (PSC) arises from workplace policies, practices, and procedures for the protection of worker psychological health and safety that are largely driven by management. Many work stress theories are based on the fundamental interaction hypothesis - that a high level of job demands (D) will lead to psychological distress and that this relationship will be offset when there are high job resources (R). However we proposed that this interaction really depends on the organizational context; in particular high levels of psychosocial safety climate will enable the safe utilization of resources to reduce demands. The study sample consisted of police constables from 23 police units (stations) with longitudinal survey responses at two time points separated by 14 months (Time 1, N=319, Time 2, N=139). We used hierarchical linear modeling to assess the effect of the proposed three-way interaction term (PSC×D×R) on change in workgroup distress variance over time. Specifically we confirmed the interaction between emotional demands and emotional resources (assessed at the individual level), in the context of unit psychosocial safety climate (aggregated individual data). As predicted, high emotional resources moderated the positive relationship between emotional demands and change in workgroup distress but only when there were high levels of unit psychosocial safety climate. Results were confirmed using a split-sample analysis. Results support psychosocial safety climate as a property of the organization and a target for higher order controls for reducing work stress. The 'right' climate enables resources to do their job. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  12. Eye-tracking Post-editing Behaviour in an Interactive Translation Prediction Environment

    DEFF Research Database (Denmark)

    Mesa-Lao, Bartolomé

    2013-01-01

    challenges faced by translators. This paper reports on a preliminary pilot test within the CasMaCat project. Based in user activity data (key-logging and eye-tracking), this project aims at defining the functionalities of a new translator's workbench focusing on post-editing and advanced computer......-aided translation methods. The main aim of this preliminary pilot was to assess one of the new features implemented in the second prototype of the workbench: the interactive translation prediction (ITP) feature. This ITP feature is set to provide translators with different suggestion as they post......-edit. For this purpose 6 translators were asked to post-edit 1,000 words from English to Spanish in five different tasks while their eye movements were being tracked. Each task was designed to test different modalities of ITP. Translators were also asked to fill out a questionnaire expressing their attitudes towards...

  13. An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

    Directory of Open Access Journals (Sweden)

    Daniel Torregrosa

    2014-09-01

    Full Text Available We present a web-based open-source tool for interactive translation prediction (ITP and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.

  14. Daily stress interacts with trait dissociation to predict sleep-related experiences in young adults.

    Science.gov (United States)

    Soffer-Dudek, Nirit; Shahar, Golan

    2011-08-01

    Building on the previously documented effects of stress and dissociation on sleep and dreaming, we examined their interactive role in general sleep-related experiences (GSEs; e.g., nightmares, falling dreams, hypnagogic hallucinations; see Watson, 2001). Stress, sleep quality, and GSEs were assessed daily for 14 days among young adults. Baseline assessment included life stress, sleep quality, psychopathology, dissociation, and related dimensions. Multilevel analyses indicated that daily stress brings about GSEs among highly dissociative young adults. Additionally, baseline trait dissociation predicted within-subject elevation in GSEs when daily stress was high. Flawed sleep-wake transitions, previously linked to dissociation and sleep-related experiences, might account for this effect. © 2011 American Psychological Association

  15. Genetic vulnerability interacts with parenting and early care education to predict increasing externalizing behavior.

    Science.gov (United States)

    Lipscomb, Shannon T; Laurent, Heidemarie; Neiderhiser, Jenae M; Shaw, Daniel S; Natsuaki, Misaki N; Reiss, David; Leve, Leslie D

    2014-01-01

    The current study examined interactions among genetic influences and children's early environments on the development of externalizing behaviors from 18 months to 6 years of age. Participants included 233 families linked through adoption (birth parents and adoptive families). Genetic influences were assessed by birth parent temperamental regulation. Early environments included both family (overreactive parenting) and out-of-home factors (center-based Early Care and Education; ECE). Overreactive parenting predicted more child externalizing behaviors. Attending center-based ECE was associated with increasing externalizing behaviors only for children with genetic liability for dysregulation. Additionally, children who were at risk for externalizing behaviors due to both genetic variability and exposure to center-based ECE were more sensitive to the effects of overreactive parenting on externalizing behavior than other children.

  16. Chronic and Daily Stressors Along With Negative Affect Interact to Predict Daily Tiredness.

    Science.gov (United States)

    Hartsell, Elizabeth N; Neupert, Shevaun D

    2017-11-01

    The present study examines the within-person relationship of daily stressors and tiredness and whether this depends on daily negative affect and individual differences in chronic stress. One hundred sixteen older adult participants were recruited via Amazon's Mechanical Turk for a 9-day daily diary study. Daily tiredness, daily stressors, and negative affect were measured each day, and chronic stress was measured at baseline. Daily stressors, daily negative affect, and chronic stress interacted to predict daily tiredness. People with high chronic stress who experienced an increase in daily negative affect were the most reactive to daily stressors in terms of experiencing an increase in daily tiredness. We also found that people with low levels of chronic stress were the most reactive to daily stressors when they experienced low levels of daily negative affect. Our results highlight the need for individualized and contextualized approaches to combating daily tiredness in older adults.

  17. A new three-particle-interaction model to predict the thermodynamic properties of different electrolytes

    International Nuclear Information System (INIS)

    Ge Xinlei; Wang Xidong; Zhang Mei; Seetharaman, Seshadri

    2007-01-01

    In this study, Guggenheim charging process, which involves the radial Boltzmann distribution, was introduced to develop a new predictive model with three parameters, ion-ion distance parameter, ion-solvent parameter, and solvation parameter. In this model, the ion-ion and ion-solvent molecule interaction are all included in the charging process, and it is independent of the temperature and solvent. This new model was applied to correlate the experimental data from literatures for 208 electrolytes aqueous solution at T = 298.15 K of which the concentration range is wide. The calculated results agreed well with the experimental ones for most electrolytes, especially for the prediction in high ionic strength. The estimation of solvation parameter S also gave that the solvation tendency for cations and anions follow a trend, which is in consistent with results published in literature. Investigations were also been made in calculations for electrolytes solutions at other temperatures and non-aqueous system, which proved this model was also feasible

  18. Interactive Digital Serious Games for the Assessment, Rehabilitation, and Prediction of Dementia

    Directory of Open Access Journals (Sweden)

    Sayed Kazmi

    2014-01-01

    Full Text Available Dementia is a serious, progressive, and often debilitating illness with no known cure, having a severe adverse effect on memory, behaviour, reasoning, and communication. A comprehensive review of current refereed research material in the use of games in this area is scarce and suffers from being orientated towards commercially available games or derivatives such as “Dr. Kawashima’s brain training.” There is much lesser concern for bespoke research grade alternatives. This review will attempt to assess the current state of the art in research orientated games for dementia, importantly identifying systems capable of prediction before the onset of the disease. It can be ascertained from the literature reviewed that there are clearly a large number of interactive computer game based mechanisms used for dementia. However, these are each highly intrusive in terms of affecting normal living and the patient is aware of being tested; furthermore their long-term or real benefits are unknown as is their effect over conventional tests. It is important to predict cognitive impairment at a stage early enough to maximise benefit from treatment and therapeutic intervention. Considering the availability, use, and increasing power of modern mobile smartphones, it is logically plausible to explore this platform for dementia healthcare.

  19. Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

    Science.gov (United States)

    Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

    2013-02-01

    This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

  20. 1Interaction between serum BDNF and aerobic fitness predicts recognition memory in healthy young adults

    Science.gov (United States)

    Whiteman, Andrew; Young, Daniel E.; He, Xuemei; Chen, Tai C.; Wagenaar, Robert C.; Stern, Chantal; Schon, Karin

    2013-01-01

    Convergent evidence from human and non-human animal studies suggests aerobic exercise and increased aerobic capacity may be beneficial for brain health and cognition. It is thought growth factors may mediate this putative relationship, particularly by augmenting plasticity mechanisms in the hippocampus, a brain region critical for learning and memory. Among these factors, glucocorticoids, brain derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-1), and vascular endothelial growth factor (VEGF), hormones that have considerable and diverse physiological importance, are thought to effect normal and exercise-induced hippocampal plasticity. Despite these predictions, relatively few published human studies have tested hypotheses that relate exercise and fitness to the hippocampus, and none have considered the potential links to all of these hormonal components. Here we present cross-sectional data from a study of recognition memory; serum BDNF, cortisol, IGF-1, and VEGF levels; and aerobic capacity in healthy young adults. We measured circulating levels of these hormones together with performance on a recognition memory task, and a standard graded treadmill test of aerobic fitness. Regression analyses demonstrated BDNF and aerobic fitness predict recognition memory in an interactive manner. In addition, IGF-1 was positively associated with aerobic fitness, but not with recognition memory. Our results may suggest an exercise adaptation-related change in the BDNF dose-response curve that relates to hippocampal memory. PMID:24269495

  1. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

    Science.gov (United States)

    Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

    2011-08-21

    Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

    Science.gov (United States)

    Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Concurrent Modeling of Hydrodynamics and Interaction Forces Improves Particle Deposition Predictions.

    Science.gov (United States)

    Jin, Chao; Ren, Carolyn L; Emelko, Monica B

    2016-04-19

    It is widely believed that media surface roughness enhances particle deposition-numerous, but inconsistent, examples of this effect have been reported. Here, a new mathematical framework describing the effects of hydrodynamics and interaction forces on particle deposition on rough spherical collectors in absence of an energy barrier was developed and validated. In addition to quantifying DLVO force, the model includes improved descriptions of flow field profiles and hydrodynamic retardation functions. This work demonstrates that hydrodynamic effects can significantly alter particle deposition relative to expectations when only the DLVO force is considered. Moreover, the combined effects of hydrodynamics and interaction forces on particle deposition on rough, spherical media are not additive, but synergistic. Notably, the developed model's particle deposition predictions are in closer agreement with experimental observations than those from current models, demonstrating the importance of inclusion of roughness impacts in particle deposition description/simulation. Consideration of hydrodynamic contributions to particle deposition may help to explain discrepancies between model-based expectations and experimental outcomes and improve descriptions of particle deposition during physicochemical filtration in systems with nonsmooth collector surfaces.

  4. Predicting Internalizing Problems in Chinese Children: the Unique and Interactive Effects of Parenting and Child Temperament

    Science.gov (United States)

    Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun

    2012-01-01

    The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children’s internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (6 – 9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children’s internalizing problems. Structural equation modeling indicated that the main effect of authoritative parenting, and the interactions of authoritarian parenting × effortful control and authoritative parenting × anger/frustration (parents’ reports only) prospectively and uniquely predicted internalizing problems. The above results did not vary by child sex and remained significant after controlling for co-occurring externalizing problems. These findings suggest that: a) children with low effortful control may be particularly susceptible to the adverse effect of authoritarian parenting, and b) the benefit of authoritative parenting may be especially important for children with high anger/frustration. PMID:23880383

  5. Predicting internalizing problems in Chinese children: the unique and interactive effects of parenting and child temperament.

    Science.gov (United States)

    Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun

    2013-08-01

    The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children's internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (aged 6-9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children's internalizing problems. Structural equation modeling indicated that the main effect of authoritative parenting and the interactions of Authoritarian Parenting × Effortful Control and Authoritative Parenting × Anger/Frustration (parents' reports only) prospectively and uniquely predicted internalizing problems. The above results did not vary by child sex and remained significant after controlling for co-occurring externalizing problems. These findings suggest that (a) children with low effortful control may be particularly susceptible to the adverse effect of authoritarian parenting and (b) the benefit of authoritative parenting may be especially important for children with high anger/frustration.

  6. Interaction between striatal volume and DAT1 polymorphism predicts working memory development during adolescence

    Directory of Open Access Journals (Sweden)

    F. Nemmi

    2018-04-01

    Full Text Available There is considerable inter-individual variability in the rate at which working memory (WM develops during childhood and adolescence, but the neural and genetic basis for these differences are poorly understood. Dopamine-related genes, striatal activation and morphology have been associated with increased WM capacity after training. Here we tested the hypothesis that these factors would also explain some of the inter-individual differences in the rate of WM development.We measured WM performance in 487 healthy subjects twice: at age 14 and 19. At age 14 subjects underwent a structural MRI scan, and genotyping of five single nucleotide polymorphisms (SNPs in or close to the dopamine genes DRD2, DAT-1 and COMT, which have previously been associated with gains in WM after WM training. We then analyzed which biological factors predicted the rate of increase in WM between ages 14 and 19.We found a significant interaction between putamen size and DAT1/SLC6A3 rs40184 polymorphism, such that TC heterozygotes with a larger putamen at age 14 showed greater WM improvement at age 19.The effect of the DAT1 polymorphism on WM development was exerted in interaction with striatal morphology. These results suggest that development of WM partially share neuro-physiological mechanism with training-induced plasticity. Keywords: Working memory, Development, Dopamine, Striatum, DAT-1, rs40184

  7. Maternal executive function, heart rate, and EEG alpha reactivity interact in the prediction of harsh parenting.

    Science.gov (United States)

    Deater-Deckard, Kirby; Bell, Martha Ann

    2017-02-01

    Do physiological and behavioral performance indicators of effortful cognitive self-regulation converge additively or interactively in their statistical prediction of individual differences in harsh parenting? To answer this question, we examined heart rate (HR) and electroencephalography alpha (α) reactivity during executive function (EF) tasks, along with observed and self-reported indicators of harsh parenting. A socioeconomically diverse sample of 115 mothers with 3- to 7-year-old children completed questionnaires and a laboratory visit. Three quarters of the mothers showed typical patterns of task reactivity that were interpretable (i.e., increases in HR and decreases in α). Among them, we found no evidence to suggest that variance in harsh parenting was associated with magnitude of HR or α reactivity independently. Instead, the physiological variables interacted to enhance the EF statistical effect. EF explained one third of the variance in harsh parenting among mothers showing the largest α decreases when accompanied by modest to moderate (rather than substantial) HR increases. Physiological indicators can clarify the role and estimation of the strength of the effect of direct behavioral measures of cognitive regulation in the etiology of harsh parenting behaviors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  8. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H; Wang, Yanli

    2018-02-06

    While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

  9. Dispositional optimism and perceived risk interact to predict intentions to learn genome sequencing results.

    Science.gov (United States)

    Taber, Jennifer M; Klein, William M P; Ferrer, Rebecca A; Lewis, Katie L; Biesecker, Leslie G; Biesecker, Barbara B

    2015-07-01

    Dispositional optimism and risk perceptions are each associated with health-related behaviors and decisions and other outcomes, but little research has examined how these constructs interact, particularly in consequential health contexts. The predictive validity of risk perceptions for health-related information seeking and intentions may be improved by examining dispositional optimism as a moderator, and by testing alternate types of risk perceptions, such as comparative and experiential risk. Participants (n = 496) had their genomes sequenced as part of a National Institutes of Health pilot cohort study (ClinSeq®). Participants completed a cross-sectional baseline survey of various types of risk perceptions and intentions to learn genome sequencing results for differing disease risks (e.g., medically actionable, nonmedically actionable, carrier status) and to use this information to change their lifestyle/health behaviors. Risk perceptions (absolute, comparative, and experiential) were largely unassociated with intentions to learn sequencing results. Dispositional optimism and comparative risk perceptions interacted, however, such that individuals higher in optimism reported greater intentions to learn all 3 types of sequencing results when comparative risk was perceived to be higher than when it was perceived to be lower. This interaction was inconsistent for experiential risk and absent for absolute risk. Independent of perceived risk, participants high in dispositional optimism reported greater interest in learning risks for nonmedically actionable disease and carrier status, and greater intentions to use genome information to change their lifestyle/health behaviors. The relationship between risk perceptions and intentions may depend on how risk perceptions are assessed and on degree of optimism. (c) 2015 APA, all rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Laura M Schwab-Reese

    Full Text Available 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.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.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.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 and violence, but

  11. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

    Science.gov (United States)

    Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang

    2017-09-18

    The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

  12. Do core interpersonal and affective traits of PCL-R psychopathy interact with antisocial behavior and disinhibition to predict violence?

    Science.gov (United States)

    Kennealy, Patrick J; Skeem, Jennifer L; Walters, Glenn D; Camp, Jacqueline

    2010-09-01

    The utility of psychopathy measures in predicting violence is largely explained by their assessment of social deviance (e.g., antisocial behavior; disinhibition). A key question is whether social deviance interacts with the core interpersonal-affective traits of psychopathy to predict violence. Do core psychopathic traits multiply the (already high) risk of violence among disinhibited individuals with a dense history of misbehavior? This meta-analysis of 32 effect sizes (N = 10,555) tested whether an interaction between the Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 2003) Interpersonal-Affective and Social Deviance scales predicted violence beyond the simple additive effects of each scale. Results indicate that Social Deviance is more uniquely predictive of violence (d = .40) than Interpersonal-Affective traits (d = .11), and these two scales do not interact (d = .00) to increase power in predicting violence. In fact, Social Deviance alone would predict better than the Interpersonal-Affective scale and any interaction in 81% and 96% of studies, respectively. These findings have fundamental practical implications for risk assessment and theoretical implications for some conceptualizations of psychopathy.

  13. The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique

    KAUST Repository

    Theofilatos, Konstantinos A.

    2013-07-12

    Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.

  14. Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

    Directory of Open Access Journals (Sweden)

    Aïsha Sahaï

    2017-10-01

    Full Text Available Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.

  15. Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction.

    Science.gov (United States)

    Cardoso, F F; Tempelman, R J

    2012-07-01

    The objectives of this work were to assess alternative linear reaction norm (RN) models for genetic evaluation of Angus cattle in Brazil. That is, we investigated the interaction between genotypes and continuous descriptors of the environmental variation to examine evidence of genotype by environment interaction (G×E) in post-weaning BW gain (PWG) and to compare the environmental sensitivity of national and imported Angus sires. Data were collected by the Brazilian Angus Improvement Program from 1974 to 2005 and consisted of 63,098 records and a pedigree file with 95,896 animals. Six models were implemented using Bayesian inference and compared using the Deviance Information Criterion (DIC). The simplest model was M(1), a traditional animal model, which showed the largest DIC and hence the poorest fit when compared with the 4 alternative RN specifications accounting for G×E. In M(2), a 2-step procedure was implemented using the contemporary group posterior means of M(1) as the environmental gradient, ranging from -92.6 to +265.5 kg. Moreover, the benefits of jointly estimating all parameters in a 1-step approach were demonstrated by M(3). Additionally, we extended M(3) to allow for residual heteroskedasticity using an exponential function (M(4)) and the best fitting (smallest DIC) environmental classification model (M(5)) specification. Finally, M(6) added just heteroskedastic residual variance to M(1). Heritabilities were less at harsh environments and increased with the improvement of production conditions for all RN models. Rank correlations among genetic merit predictions obtained by M(1) and by the best fitting RN models M(3) (homoskedastic) and M(5) (heteroskedastic) at different environmental levels ranged from 0.79 and 0.81, suggesting biological importance of G×E in Brazilian Angus PWG. These results suggest that selection progress could be optimized by adopting environment-specific genetic merit predictions. The PWG environmental sensitivity of

  16. Autonomic and Adrenocortical Interactions Predict Mental Health in Late Adolescence: The TRAILS Study.

    Science.gov (United States)

    Nederhof, Esther; Marceau, Kristine; Shirtcliff, Elizabeth A; Hastings, Paul D; Oldehinkel, Albertine J

    2015-07-01

    The present study is informed by the theory of allostatic load to examine how multiple stress responsive biomarkers are related to mental health outcomes. Data are from the TRAILS study, a large prospective population study of 715 Dutch adolescents (50.9 % girls), assessed at 16.3 and 19.1 years. Reactivity measures of the hypothalamic pituitary-adrenal (HPA) axis and autonomic nervous system (ANS) biomarkers (heart rate, HR; respiratory sinus arrhythmia, RSA; and pre-ejection period, PEP) to a social stress task were used to predict concurrent and longitudinal changes in internalizing and externalizing symptoms. Hierarchical linear modeling revealed relatively few single effects for each biomarker with the exception that high HR reactivity predicted concurrent internalizing problems in boys. More interestingly, interactions were found between HPA-axis reactivity and sympathetic and parasympathetic reactivity. Boys with high HPA reactivity and low RSA reactivity had the largest increases in internalizing problems from 16 to 19 years. Youth with low HPA reactivity along with increased ANS activation characterized by both decreases in RSA and decreases in PEP had the most concurrent externalizing problems, consistent with broad theories of hypo-arousal. Youth with high HPA reactivity along with increases in RSA but decreases in PEP also had elevated concurrent externalizing problems, which increased over time, especially within boys. This profile illustrates the utility of examining the parasympathetic and sympathetic components of the ANS which can act in opposition to one another to achieve, overall, stress responsivity. The framework of allostasis and allostatic load is supported in that examination of multiple biomarkers working together in concert was of value in understanding mental health problems concurrently and longitudinally. Findings argue against an additive panel of risk and instead illustrate the dynamic interplay of stress physiology systems.

  17. A strategy for interaction site prediction between phospho-binding modules and their partners identified from proteomic data.

    Science.gov (United States)

    Aucher, Willy; Becker, Emmanuelle; Ma, Emilie; Miron, Simona; Martel, Arnaud; Ochsenbein, Françoise; Marsolier-Kergoat, Marie-Claude; Guerois, Raphaël

    2010-12-01

    Small and large scale proteomic technologies are providing a wealth of potential interactions between proteins bearing phospho-recognition modules and their substrates. Resulting interaction maps reveal such a dense network of interactions that the functional dissection and understanding of these networks often require to break specific interactions while keeping the rest intact. Here, we developed a computational strategy, called STRIP, to predict the precise interaction site involved in an interaction with a phospho-recognition module. The method was validated by a two-hybrid screen carried out using the ForkHead Associated (FHA)1 domain of Rad53, a key protein of Saccharomyces cerevisiae DNA checkpoint, as a bait. In this screen we detected 11 partners, including Cdc7 and Cdc45, essential components of the DNA replication machinery. FHA domains are phospho-threonine binding modules and the threonines involved in both interactions could be predicted using the STRIP strategy. The threonines T484 and T189 in Cdc7 and Cdc45, respectively, were mutated and loss of binding could be monitored experimentally with the full-length proteins. The method was further tested for the analysis of 63 known Rad53 binding partners and provided several key insights regarding the threonines likely involved in these interactions. The STRIP method relies on a combination of conservation, phosphorylation likelihood, and binding specificity criteria and can be accessed via a web interface at http://biodev.extra.cea.fr/strip/.

  18. A Strategy for Interaction Site Prediction between Phospho-binding Modules and their Partners Identified from Proteomic Data*

    Science.gov (United States)

    Aucher, Willy; Becker, Emmanuelle; Ma, Emilie; Miron, Simona; Martel, Arnaud; Ochsenbein, Françoise; Marsolier-Kergoat, Marie-Claude; Guerois, Raphaël

    2010-01-01

    Small and large scale proteomic technologies are providing a wealth of potential interactions between proteins bearing phospho-recognition modules and their substrates. Resulting interaction maps reveal such a dense network of interactions that the functional dissection and understanding of these networks often require to break specific interactions while keeping the rest intact. Here, we developed a computational strategy, called STRIP, to predict the precise interaction site involved in an interaction with a phospho-recognition module. The method was validated by a two-hybrid screen carried out using the ForkHead Associated (FHA)1 domain of Rad53, a key protein of Saccharomyces cerevisiae DNA checkpoint, as a bait. In this screen we detected 11 partners, including Cdc7 and Cdc45, essential components of the DNA replication machinery. FHA domains are phospho-threonine binding modules and the threonines involved in both interactions could be predicted using the STRIP strategy. The threonines T484 and T189 in Cdc7 and Cdc45, respectively, were mutated and loss of binding could be monitored experimentally with the full-length proteins. The method was further tested for the analysis of 63 known Rad53 binding partners and provided several key insights regarding the threonines likely involved in these interactions. The STRIP method relies on a combination of conservation, phosphorylation likelihood, and binding specificity criteria and can be accessed via a web interface at http://biodev.extra.cea.fr/strip/. PMID:20733106

  19. Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

    Science.gov (United States)

    Poovathingal, Savio

    An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of

  20. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  1. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  2. Predicting Risky Sexual Behavior: the Unique and Interactive Roles of Childhood Conduct Disorder Symptoms and Callous-Unemotional Traits.

    Science.gov (United States)

    Anderson, Sarah L; Zheng, Yao; McMahon, Robert J

    2017-08-01

    Conduct disorder (CD) symptoms and callous-unemotional (CU) traits have been shown to be uniquely associated with risky sexual behavior (RSB) in adolescence and early adulthood, yet their interactive role in predicting RSB remains largely unknown. This study aimed to investigate the predictive value of CD symptoms and CU traits, as well as their interaction, on several RSB outcomes in adolescence and early adulthood. A total of 683 participants (41.7 % female, 47.4 % African American) were followed annually and self-reported age of first sexual intercourse, frequency of condom use, pregnancy, contraction of sexually transmitted infections, and engagement in sexual solicitation from grade 7 to 2-years post-high school. CD symptoms predicted age of first sexual intercourse, condom use, and sexual solicitation. CU traits predicted age of first sexual intercourse and pregnancy. Their interaction predicted a composite score of these RSBs such that CD symptoms positively predicted the composite score among those with high levels of CU traits but not among those with low levels of CU traits. The current findings provide information regarding the importance of both CD symptoms and CU traits in understanding adolescent and early adulthood RSB, as well as the benefits of examining multiple RSB outcomes during this developmental period. These findings have implications for the development and implementation of preventive efforts to target these risky behaviors among adolescents and young adults.

  3. Interaction between Digestive Strategy and Niche Specialization Predicts Speciation Rates across Herbivorous Mammals.

    Science.gov (United States)

    Tran, Lucy A P

    2016-04-01

    Biotic and abiotic factors often are treated as mutually exclusive drivers of diversification processes. In this framework, ecological specialists are expected to have higher speciation rates than generalists if abiotic factors are the primary controls on species diversity but lower rates if biotic interactions are more important. Speciation rate is therefore predicted to positively correlate with ecological specialization in the purely abiotic model but negatively correlate in the biotic model. In this study, I show that the positive relationship between ecological specialization and speciation expected from the purely abiotic model is recovered only when a species-specific trait, digestive strategy, is modeled in the terrestrial, herbivorous mammals (Mammalia). This result suggests a more nuanced model in which the response of specialized lineages to abiotic factors is dependent on a biological trait. I also demonstrate that the effect of digestive strategy on the ecological specialization-speciation rate relationship is not due to a difference in either the degree of ecological specialization or the speciation rate between foregut- and hindgut-fermenting mammals. Together, these findings suggest that a biological trait, alongside historical abiotic events, played an important role in shaping mammal speciation at long temporal and large geographic scales.

  4. Suspicion of Motives Predicts Minorities' Responses to Positive Feedback in Interracial Interactions.

    Science.gov (United States)

    Major, Brenda; Kunstman, Jonathan W; Malta, Brenna D; Sawyer, Pamela J; Townsend, Sarah S M; Mendes, Wendy Berry

    2016-01-01

    Strong social and legal norms in the United States discourage the overt expression of bias against ethnic and racial minorities, increasing the attributional ambiguity of Whites' positive behavior to ethnic minorities. Minorities who suspect that Whites' positive overtures toward minorities are motivated more by their fear of appearing racist than by egalitarian attitudes may regard positive feedback they receive from Whites as disingenuous. This may lead them to react to such feedback with feelings of uncertainty and threat. Three studies examined how suspicion of motives relates to ethnic minorities' responses to receiving positive feedback from a White peer or same-ethnicity peer (Experiment 1), to receiving feedback from a White peer that was positive or negative (Experiment 2), and to receiving positive feedback from a White peer who did or did not know their ethnicity (Experiment 3). As predicted, the more suspicious Latinas were of Whites' motives for behaving positively toward minorities in general, the more they regarded positive feedback from a White peer who knew their ethnicity as disingenuous and the more they reacted with cardiovascular reactivity characteristic of threat/avoidance, increased feelings of stress, heightened uncertainty, and decreased self-esteem. We discuss the implications for intergroup interactions of perceptions of Whites' motives for nonprejudiced behavior.

  5. Suspicion of Motives Predicts Minorities’ Responses to Positive Feedback in Interracial Interactions

    Science.gov (United States)

    Major, Brenda; Kunstman, Jonathan W.; Malta, Brenna D.; Sawyer, Pamela J.; Townsend, Sarah S. M.; Mendes, Wendy Berry

    2015-01-01

    Strong social and legal norms in the United States discourage the overt expression of bias against ethnic and racial minorities, increasing the attributional ambiguity of Whites’ positive behavior to ethnic minorities. Minorities who suspect that Whites’ positive overtures toward minorities are motivated more by their fear of appearing racist than by egalitarian attitudes may regard positive feedback they receive from Whites as disingenuous. This may lead them to react to such feedback with feelings of uncertainty and threat. Three studies examined how suspicion of motives relates to ethnic minorities’ responses to receiving positive feedback from a White peer or same-ethnicity peer (Experiment 1), to receiving feedback from a White peer that was positive or negative (Experiment 2), and to receiving positive feedback from a White peer who did or did not know their ethnicity (Experiment 3). As predicted, the more suspicious Latinas were of Whites’ motives for behaving positively toward minorities in general, the more they regarded positive feedback from a White peer who knew their ethnicity as disingenuous and the more they reacted with cardiovascular reactivity characteristic of threat/avoidance, increased feelings of stress, heightened uncertainty, and decreased self-esteem. We discuss the implications for intergroup interactions of perceptions of Whites’ motives for nonprejudiced behavior. PMID:26688594

  6. Interaction between hippocampal and striatal systems predicts subsequent consolidation of motor sequence memory.

    Directory of Open Access Journals (Sweden)

    Geneviève Albouy

    Full Text Available The development of fast and reproducible motor behavior is a crucial human capacity. The aim of the present study was to address the relationship between the implementation of consistent behavior during initial training on a sequential motor task (the Finger Tapping Task and subsequent sleep-dependent motor sequence memory consolidation, using functional magnetic resonance imaging (fMRI and total sleep deprivation protocol. Our behavioral results indicated significant offline gains in performance speed after sleep whereas performance was only stabilized, but not enhanced, after sleep deprivation. At the cerebral level, we previously showed that responses in the caudate nucleus increase, in parallel to a decrease in its functional connectivity with frontal areas, as performance became more consistent. Here, the strength of the competitive interaction, assessed through functional connectivity analyses, between the caudate nucleus and hippocampo-frontal areas during initial training, predicted delayed gains in performance at retest in sleepers but not in sleep-deprived subjects. Moreover, during retest, responses increased in the hippocampus and medial prefrontal cortex in sleepers whereas in sleep-deprived subjects, responses increased in the putamen and cingulate cortex. Our results suggest that the strength of the competitive interplay between the striatum and the hippocampus, participating in the implementation of consistent motor behavior during initial training, conditions subsequent motor sequence memory consolidation. The latter process appears to be supported by a reorganisation of cerebral activity in hippocampo-neocortical networks after sleep.

  7. The interaction between self-regulation and motivation prospectively predicting problem behavior in adolescence.

    Science.gov (United States)

    Rhodes, Jessica D; Colder, Craig R; Trucco, Elisa M; Speidel, Carolyn; Hawk, Larry W; Lengua, Liliana J; Das Eiden, Rina; Wieczorek, William

    2013-01-01

    A large literature suggests associations between self-regulation and motivation and adolescent problem behavior; however, this research has mostly pitted these constructs against one another or tested them in isolation. Following recent neural-systems based theories (e.g., Ernst & Fudge, 2009 ), the present study investigated the interactions between self-regulation and approach and avoidance motivation prospectively predicting delinquency and depressive symptoms in early adolescence. The community sample included 387 adolescents aged 11 to 13 years old (55% female; 17% minority). Laboratory tasks were used to assess self-regulation and approach and avoidance motivation, and adolescent self-reports were used to measure depressive symptoms and delinquency. Analyses suggested that low levels of approach motivation were associated with high levels of depressive symptoms, but only at high levels of self-regulation (p = .01). High levels of approach were associated with high levels of rule breaking, but only at low levels of self-regulation (p theories that posit integration of motivational and self-regulatory individual differences via moderational models to understand adolescent problem behavior.

  8. Emotionally laden impulsivity interacts with affect in predicting addictive use of online sexual activity in men.

    Science.gov (United States)

    Wéry, Aline; Deleuze, Jory; Canale, Natale; Billieux, Joël

    2018-01-01

    The interest in studying addictive use of online sexual activities (OSA) has grown sharply over the last decade. Despite the burgeoning number of studies conceptualizing the excessive use of OSA as an addictive disorder, few have tested its relations to impulsivity, which is known to constitute a hallmark of addictive behaviors. To address this missing gap in the literature, we tested the relationships between addictive OSA use, impulsivity traits, and affect among a convenience sample of men (N=182; age, M=29.17, SD = 9.34), building upon a theoretically driven model that distinguishes the various facets of impulsivity. Results showed that negative urgency (an impulsivity trait reflecting the tendency to act rashly in negative emotional states) and negative affect interact in predicting addictive OSA use. These results highlight the pivotal role played by negative urgency and negative affect in addictive OSA use, supporting the relevance of psychological interventions that focus on improving emotional regulation (e.g., to reduce negative affect and learn healthier coping strategies) to mitigate excessive use of OSA. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Prediction of the amount of hydrogen generated during a molten fuel-coolant interaction

    International Nuclear Information System (INIS)

    Matthern, G.E.; Neuman, J.E.; Madsen, W.W.; Close, J.A.

    1990-01-01

    The model in development predicts the production of hydrogen as a result of a molten fuel-coolant interaction in a water-cooled nuclear reactor. It has three interrelated modules: kinetics, heat transfer, and hydrodynamics. Second and third order rates are assumed for uranium and aluminum respectively, the chosen fuel and cladding. Heat is generated by chemical reaction and radioactive decay and dissipated through radiation and convection. Dispersion of the melt as it descends through a pool of water is modeled using the Weber number, which ratios the shear forces due to the relative velocities of the fluid and the metal to the surface tension of the metal. Hydrogen generation is sensitive to the initial melt temperature and to the assumptions made about the modes of heat transfer, but not the the impact velocity of the metal particle. The hydrogen generation per unit mass of uranium generally increases as the initial particle size decreases suggesting that the kinetics rather than the heat transfer controls the energy balance

  10. Prediction of d^0 magnetism in self-interaction corrected density functional theory

    Science.gov (United States)

    Das Pemmaraju, Chaitanya

    2010-03-01

    Over the past couple of years, the phenomenon of ``d^0 magnetism'' has greatly intrigued the magnetism community [1]. Unlike conventional magnetic materials, ``d^0 magnets'' lack any magnetic ions with open d or f shells but surprisingly, exhibit signatures of ferromagnetism often with a Curie temperature exceeding 300 K. Current research in the field is geared towards trying to understand the mechanism underlying this observed ferromagnetism which is difficult to explain within the conventional m-J paradigm [1]. The most widely studied class of d^0 materials are un-doped and light element doped wide gap Oxides such as HfO2, MgO, ZnO, TiO2 all of which have been put forward as possible d0 ferromagnets. General experimental trends suggest that the magnetism is a feature of highly defective samples leading to the expectation that the phenomenon must be defect related. In particular, based on density functional theory (DFT) calculations acceptor defects formed from the O-2p states in these Oxides have been proposed as being responsible for the ferromagnetism [2,3]. However. predicting magnetism originating from 2p orbitals is a delicate problem, which depends on the subtle interplay between covalency and Hund's coupling. DFT calculations based on semi-local functionals such as the local spin-density approximation (LSDA) can lead to qualitative failures on several fronts. On one hand the excessive delocalization of spin-polarized holes leads to half-metallic ground states and the expectation of room-temperature ferromagnetism. On the other hand, in some cases a magnetic ground state may not be predicted at all as the Hund's coupling might be under estimated. Furthermore, polaronic distortions which are often a feature of acceptor defects in Oxides are not predicted [4,5]. In this presentation, we argue that the self interaction error (SIE) inherent to semi-local functionals is responsible for the failures of LSDA and demonstrate through various examples that beyond

  11. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

    Science.gov (United States)

    Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

    2018-05-08

    Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on

  12. Progressive and Regressive Developmental Changes in Neural Substrates for Face Processing: Testing Specific Predictions of the Interactive Specialization Account

    Science.gov (United States)

    Joseph, Jane E.; Gathers, Ann D.; Bhatt, Ramesh S.

    2011-01-01

    Face processing undergoes a fairly protracted developmental time course but the neural underpinnings are not well understood. Prior fMRI studies have only examined progressive changes (i.e. increases in specialization in certain regions with age), which would be predicted by both the Interactive Specialization (IS) and maturational theories of…

  13. Predicting Day-to-Day Changes in Students' School-Related Affect from Daily Academic Experiences and Social Interactions

    Science.gov (United States)

    Altermatt, Ellen Rydell

    2015-01-01

    This study examined the role that everyday academic successes and failures--and the interactions with family members and peers that follow these events--play in predicting day-to-day changes in children's emotional responses to school. Middle school students (N = 101; mean age = 11.62 years) completed daily assessments of their academic…

  14. Observed fearlessness and positive parenting interact to predict childhood callous-unemotional behaviors among low-income boys.

    Science.gov (United States)

    Waller, Rebecca; Shaw, Daniel S; Hyde, Luke W

    2017-03-01

    Callous-unemotional behaviors identify children at risk for severe and chronic antisocial behavior. Research is needed to establish pathways from temperament and parenting factors that give rise to callous-unemotional behaviors, including interactions of positive versus harsh parenting with child fearlessness. Multimethod data, including parent reports and observations of parent and child behavior, were drawn from a prospective, longitudinal sample of low-income boys (N = 310) with assessments at 18, 24, and 42 months, and at ages 10-12 years old. Parent-reported callous-unemotional, oppositional, and attention-deficit factors were separable at 42 months. Callous-unemotional behaviors at 42 months predicted callous-unemotional behaviors at ages 10-12, accounting for earlier oppositional and attention-deficit behaviors and self-reported child delinquency at ages 10-12. Observations of fearlessness at 24 months predicted callous-unemotional behaviors at 42 months, but only when parents exhibited low observed levels of positive parenting. The interaction of fearlessness and low positive parenting indirectly predicted callous-unemotional behaviors at 10-12 via callous-unemotional behaviors at 42 months. Early fearlessness interacts with low positive parenting to predict early callous-unemotional behaviors, with lasting effects of this person-by-context interaction on callous-unemotional behaviors into late childhood. © 2016 Association for Child and Adolescent Mental Health.

  15. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Zheng Huiru

    2009-01-01

    Full Text Available Abstract Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases and non-interacting proteins (negative cases are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

  16. Interaction effects in the theory of planned behaviour: Predicting fruit and vegetable consumption in three prospective cohorts.

    Science.gov (United States)

    Kothe, Emily J; Mullan, Barbara A

    2015-09-01

    The theory of planned behaviour (TPB) has been criticized for not including interactions between major constructs thought to underlie behaviour. This study investigated the application of the TPB to the prediction of fruit and vegetable consumption across three prospective cohorts. The primary aim of the study was to investigate whether interactions between major constructs in the theory would increase the ability of the model to predict intention to consume fruit and vegetables (i.e., attitude × perceived behavioural control [PBC], subjective norm × PBC, subjective norm × attitude) and self-reported fruit and vegetable intake (i.e., PBC × intention). Secondary data analysis from three cohorts: One predictive study (cohort 1) and two intervention studies (cohorts 2 and 3). Participants completed a TPB measure at baseline and a measure of fruit and vegetable intake at 1 week (cohort 1; n = 90) or 1 month (cohorts 2 and 3; n = 296). Attitude moderated the impact of PBC on intention. PBC moderated the impact of intention on behaviour at 1 week but not 1 month. The variance accounted for by the interactions was small. However, the presence of interactions between constructs within the TPB demonstrates a need to consider interactions between variables within the TPB in both theoretical and applied research using the model. © 2014 The British Psychological Society.

  17. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion.

    Science.gov (United States)

    Rosenthal, Sara Brin; Twomey, Colin R; Hartnett, Andrew T; Wu, Hai Shan; Couzin, Iain D

    2015-04-14

    Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.

  18. Interaction of species traits and environmental disturbance predicts invasion success of aquatic microorganisms.

    Directory of Open Access Journals (Sweden)

    Elvira Mächler

    Full Text Available Factors such as increased mobility of humans, global trade and climate change are affecting the range of many species, and cause large-scale translocations of species beyond their native range. Many introduced species have a strong negative influence on the new local environment and lead to high economic costs. There is a strong interest to understand why some species are successful in invading new environments and others not. Most of our understanding and generalizations thereof, however, are based on studies of plants and animals, and little is known on invasion processes of microorganisms. We conducted a microcosm experiment to understand factors promoting the success of biological invasions of aquatic microorganisms. In a controlled lab experiment, protist and rotifer species originally isolated in North America invaded into a natural, field-collected community of microorganisms of European origin. To identify the importance of environmental disturbances on invasion success, we either repeatedly disturbed the local patches, or kept them as undisturbed controls. We measured both short-term establishment and long-term invasion success, and correlated it with species-specific life-history traits. We found that environmental disturbances significantly affected invasion success. Depending on the invading species' identity, disturbances were either promoting or decreasing invasion success. The interaction between habitat disturbance and species identity was especially pronounced for long-term invasion success. Growth rate was the most important trait promoting invasion success, especially when the species invaded into a disturbed local community. We conclude that neither species traits nor environmental factors alone conclusively predict invasion success, but an integration of both of them is necessary.

  19. Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

    Science.gov (United States)

    Chen, Lei; Zeng, Wei-Ming; Cai, Yu-Dong; Feng, Kai-Yan; Chou, Kuo-Chen

    2012-01-01

    The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels. PMID:22514724

  20. Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation.

    Directory of Open Access Journals (Sweden)

    Luigi Capoferri

    Full Text Available Prediction of human Cytochrome P450 (CYP binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD simulations and Linear Interaction Energy (LIE theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE of 4.1 kJ mol-1 and a standard error in prediction (SDEP in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units.

  1. Stability and Predictive Validity of the Parent-Child Sleep Interactions Scale: A Longitudinal Study Among Preschoolers.

    Science.gov (United States)

    Barrios, Chelsey S; Jay, Samantha Y; Smith, Victoria C; Alfano, Candice A; Dougherty, Lea R

    2018-01-01

    Little research has examined the processes underlying children's persistent sleep problems and links with later psychopathology. The current study examined the stability of parent-child sleep interactions as assessed with the parent-reported Parent-Child Sleep Interactions Scale (PSIS) and examined whether sleep interactions in preschool-age children predict sleep problems and psychiatric symptoms later in childhood. Participants included 108 preschool-age children (50% female) and their parents. Parents completed the PSIS when children were 3-5 years (T1) and again when they were 6-9 years (T2). The PSIS includes three subscales-Sleep Reinforcement (reassurance of child sleep behaviors), Sleep Conflict (parent-child conflict at bedtime), Sleep Dependence (difficulty going to sleep without parent)-and a total score. Higher scores indicate more problematic bedtime interactions. Children's sleep problems and psychiatric symptoms at T1 and T2 were assessed with a clinical interview. PSIS scores were moderately stable from T1 to T2, and the factor structure of the PSIS remained relatively consistent over time. Higher total PSIS scores at T1 predicted increases in children's sleep problems at T2. Higher PSIS Sleep Conflict scores at T1 predicted increases in oppositional defiant disorder symptoms at T2. Children with more sleep problems and higher PSIS Sleep Reinforcement scores at T1 showed increases in attention deficit/hyperactivity disorder, depressive, and anxiety symptoms at T2. These findings provide evidence for the predictive validity of the PSIS and highlight the importance of early parent-child sleep interactions in the development of sleep and psychiatric symptoms in childhood. Parent-child sleep interactions may serve as a useful target for interventions.

  2. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels.

    Science.gov (United States)

    Van der Putten, Wim H; Macel, Mirka; Visser, Marcel E

    2010-07-12

    Current predictions on species responses to climate change strongly rely on projecting altered environmental conditions on species distributions. However, it is increasingly acknowledged that climate change also influences species interactions. We review and synthesize literature information on biotic interactions and use it to argue that the abundance of species and the direction of selection during climate change vary depending on how their trophic interactions become disrupted. Plant abundance can be controlled by aboveground and belowground multitrophic level interactions with herbivores, pathogens, symbionts and their enemies. We discuss how these interactions may alter during climate change and the resulting species range shifts. We suggest conceptual analogies between species responses to climate warming and exotic species introduced in new ranges. There are also important differences: the herbivores, pathogens and mutualistic symbionts of range-expanding species and their enemies may co-migrate, and the continuous gene flow under climate warming can make adaptation in the expansion zone of range expanders different from that of cross-continental exotic species. We conclude that under climate change, results of altered species interactions may vary, ranging from species becoming rare to disproportionately abundant. Taking these possibilities into account will provide a new perspective on predicting species distribution under climate change.

  3. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  4. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  5. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Gao, Shangzhi; Nguyen, Nam Ninh; Fan, Mengyuan; Jin, Jingjing; Liu, Bing; Zhao, Liang; Xiong, Geng; Tan, Min; Li, Shijun; Wong, Limsoon

    2014-04-08

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both

  6. Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

    Science.gov (United States)

    Will, Sebastian; Jabbari, Hosna

    2016-01-01

    RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free

  7. Evolutionary relationships can be more important than abiotic conditions in predicting the outcome of plant-plant interactions

    Science.gov (United States)

    Soliveres, Santiago; Torices, Rubén; Maestre, Fernando T.

    2015-01-01

    Positive and negative plant-plant interactions are major processes shaping plant communities. They are affected by environmental conditions and evolutionary relationships among the interacting plants. However, the generality of these factors as drivers of pairwise plant interactions and their combined effects remain virtually unknown. We conducted an observational study to assess how environmental conditions (altitude, temperature, irradiance and rainfall), the dispersal mechanism of beneficiary species and evolutionary relationships affected the co-occurrence of pairwise interactions in 11 Stipa tenacissima steppes located along an environmental gradient in Spain. We studied 197 pairwise plant-plant interactions involving the two major nurse plants (the resprouting shrub Quercus coccifera and the tussock grass S. tenacissima) found in these communities. The relative importance of the studied factors varied with the nurse species considered. None of the factors studied were good predictors of the co-ocurrence between S. tenacissima and its neighbours. However, both the dispersal mechanism of the beneficiary species and the phylogenetic distance between interacting species were crucial factors affecting the co-occurrence between Q. coccifera and its neighbours, while climatic conditions (irradiance) played a secondary role. Values of phylogenetic distance between 207-272.8 Myr led to competition, while values outside this range or fleshy-fruitness in the beneficiary species led to positive interactions. The low importance of environmental conditions as a general driver of pairwise interactions was caused by the species-specific response to changes in either rainfall or radiation. This result suggests that factors other than climatic conditions must be included in theoretical models aimed to generally predict the outcome of plant-plant interactions. Our study helps to improve current theory on plant-plant interactions and to understand how these interactions can

  8. Consumer-resource theory predicts dynamic transitions between outcomes of interspecific interactions

    Science.gov (United States)

    Holland, J. Nathaniel; DeAngelis, Donald L.

    2009-01-01

    Interactions between two populations are often defined by their interaction outcomes; that is, the positive, neutral, or negative effects of species on one another. Yet, signs of outcomes are not absolute, but vary with the biotic and abiotic contexts of interactions. Here, we develop a general theory for transitions between outcomes based on consumer-resource (C-R) interactions in which one or both species exploit the other as a resource. Simple models of C-R interactions revealed multiple equilibria, including one for species coexistence and others for extinction of one or both species, indicating that species densities alone could determine the fate of interactions. All possible outcomes (+ +), (+ -), (- -), (+ 0), (- 0), (0 0) of species coexistence emerged merely through changes in parameter values of C-R interactions, indicating that variation in C-R interactions resulting from biotic and abiotic conditions could determine shifts in outcomes. These results suggest that C-R interactions can provide a broad mechanism for understanding context- and density-dependent transitions between interaction outcomes.

  9. Thin slices of friendship: do non-verbal behaviors predict first impressions during getting acquainted interactions?

    OpenAIRE

    Urgancı, Betül

    2017-01-01

    Cataloged from PDF version of article. Thesis (M.S.): Bilkent University, Department of Psychology, İhsan Doğramacı Bilkent University, 2017. Includes bibliographical references (leaves 28-32). Does a very brief observation of a person predict first impressions? Prior research has shown that these brief observations, called thin slices, predict many psychological outcomes such as individual performance. However, there is a not much research investigating whether thin slices predict f...

  10. Assessment of NASA and RAE viscous-inviscid interaction methods for predicting transonic flow over nozzle afterbodies

    Science.gov (United States)

    Putnam, L. E.; Hodges, J.

    1983-01-01

    The Langley Research Center of the National Aeronautics and Space Administration and the Royal Aircraft Establishment have undertaken a cooperative program to conduct an assessment of their patched viscous-inviscid interaction methods for predicting the transonic flow over nozzle afterbodies. The assessment was made by comparing the predictions of the two methods with experimental pressure distributions and boattail pressure drag for several convergent circular-arc nozzle configurations. Comparisons of the predictions of the two methods with the experimental data showed that both methods provided good predictions of the flow characteristics of nozzles with attached boundary layer flow. The RAE method also provided reasonable predictions of the pressure distributions and drag for the nozzles investigated that had separated boundary layers. The NASA method provided good predictions of the pressure distribution on separated flow nozzles that had relatively thin boundary layers. However, the NASA method was in poor agreement with experiment for separated nozzles with thick boundary layers due primarily to deficiencies in the method used to predict the separation location.

  11. Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Eric A Yen

    2014-05-01

    Full Text Available Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and

  12. A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

    Directory of Open Access Journals (Sweden)

    Mengqu Ge

    2016-02-01

    Full Text Available As one large class of non-coding RNAs (ncRNAs, long ncRNAs (lncRNAs have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI. LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR and protein-based collaborative filtering (ProCF. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

  13. Interactions of age and cognitive functions in predicting decision making under risky conditions over the life span.

    Science.gov (United States)

    Brand, Matthias; Schiebener, Johannes

    2013-01-01

    Little is known about how normal healthy aging affects decision-making competence. In this study 538 participants (age 18-80 years) performed the Game of Dice Task (GDT). Subsamples also performed the Iowa Gambling Task as well as tasks measuring logical thinking and executive functions. In a moderated regression analysis, the significant interaction between age and executive components indicates that older participants with good executive functioning perform well on the GDT, while older participants with reduced executive functions make more risky choices. The same pattern emerges for the interaction of age and logical thinking. Results demonstrate that age and cognitive functions act in concert in predicting the decision-making performance.

  14. Prediction of transport phenomena in near and far field: interaction solid phase/fluid phase

    International Nuclear Information System (INIS)

    Mingarro, E.

    1995-01-01

    The prediction of transport phenomena in near and far field is presented in the present report. The study begins with the analysis of solid phases stability: solubility of storage waste: UO 2 and solubility of radionuclides the redox and sorption-desorption conditions are the last aspects studied to predict the transport phenomena

  15. Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

    Science.gov (United States)

    Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

    2017-06-07

    Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

  16. Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

    Directory of Open Access Journals (Sweden)

    Massaine Bandeira e Sousa

    2017-06-01

    Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

  17. Theoretical model for the hydrogen-material interaction as a basis for prediction of the material mechanical properties

    International Nuclear Information System (INIS)

    Indeitsev, D.A.; Polyanskiy, V.A.; Sukhanov, A.A.; Belyaev, A.A.

    2009-01-01

    The natural law concentration of hydrogen inside the materials has a distribution over the different binding energies. This distribution is changing under the mechanical tension. The model of interaction of the small hydrogen concentration with materials provides one with an instrument for modeling the materials fatigue and destruction, as well as the prediction of material properties during exploitation. The well-known models are of the phenomenological nature. However if one takes into account the physical mechanism then one obtains an accurate model and the instrument for the reliable prediction. The two-continuum model of the solid material is a substantiation for the present study. This model describes the interaction between the low concentration of hydrogen and the material. The redistribution of the hydrogen between the different binding energy levels is taken into account, too

  18. Predicting Job Crafting From the Socially Embedded Perspective: The Interactive Effect of Job Autonomy, Social Skill, and Employee Status

    OpenAIRE

    Sekiguchi, Tomoki; Li, Jie; Hosomi, Masaki

    2017-01-01

    Job crafting represents the bottom-up process of change employees make in their work boundaries and plays an important role in the management of organizational change. Following the socially embedded perspective, we examine the roles of job autonomy, social skill, and employee status in predicting job crafting. Study 1 with a sample of 509 part-time employees found that job autonomy and social skill not only directly but also interactively influenced job crafting. Study 2 with a sample of 564...

  19. Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

    Science.gov (United States)

    An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

    2017-08-18

    Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

  20. Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

    Directory of Open Access Journals (Sweden)

    Banjanovic-Mehmedovic Lejla

    2016-01-01

    Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

  1. Prediction of gradient retention data for hydrophilic interaction liquid chromatographic separation of native and fluorescently labeled oligosaccharides.

    Science.gov (United States)

    Vaňková, Nikola; Česla, Petr

    2017-02-17

    In this work, we have investigated the predictive properties of mixed-mode retention model and oligomeric mixed-mode model, taking into account the contribution of monomeric units to the retention, in hydrophilic interaction liquid chromatography. The gradient retention times of native maltooligosaccharides and their fluorescent derivatives were predicted in the oligomeric series with number of monomeric glucose units in the range from two to seven. The maltooligosaccharides were separated on a packed column with carbamoyl-bonded silica stationary phase and 15 gradient profiles with different initial and final mobile phase composition were used with the gradient times 5; 7.5 and 10min. The predicted gradient retention times were compared for calculations based on isocratic retention data and gradient retention data, which provided better accuracy of the results. By comparing two different mobile phase additives, the more accurate retention times were predicted in mobile phases containing ammonium acetate. The acidic derivatives, prepared by reaction of an oligosaccharide with 2-aminobenzoic acid or 8-aminonaphthalene-1,3,6-trisulfonic acid, provided more accurate predictions of the retention data in comparison to native oligosaccharides or their neutral derivatives. The oligomeric mixed-mode model allowed prediction of gradient retention times using only one gradient profile, which significantly speeded-up the method development. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Prediction of Physical Activity Level Using Processes of Change From the Transtheoretical Model: Experiential, Behavioral, or an Interaction Effect?

    Science.gov (United States)

    Romain, Ahmed Jérôme; Horwath, Caroline; Bernard, Paquito

    2018-01-01

    The purpose of the present study was to compare prediction of physical activity (PA) by experiential or behavioral processes of change (POCs) or an interaction between both types of processes. A cross-sectional study. This study was conducted using an online questionnaire. A total of 394 participants (244 women, 150 men), with a mean age of 35.12 ± 12.04 years and a mean body mass index of 22.97 ± 4.25 kg/m 2 were included. Participants completed the Processes of Change, Stages of Change questionnaires, and the International Physical Activity Questionnaire to evaluate self-reported PA level (total, vigorous, and moderate PA). Hierarchical multiple regression models were used to test the prediction of PA level. For both total PA (β = .261; P behavioral POCs were a significant predictor. Regarding moderate PA, only the interaction between experiential and behavioral POCs was a significant predictor (β = .123; P = .017). Our results provide confirmation that behavioral processes are most prominent in PA behavior. Nevertheless, it is of interest to note that the interaction between experiential and behavioral POCs was the only element predicting moderate PA level. Experiential processes were not associated with PA level.

  3. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

    Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  4. Simplified method to predict mutual interactions of human transcription factors based on their primary structure

    KAUST Repository

    Schmeier, Sebastian; Jankovic, Boris R.; Bajic, Vladimir B.

    2011-01-01

    ). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting

  5. Peptide retention prediction using hydrophilic interaction liquid chromatography coupled to mass spectrometry.

    Science.gov (United States)

    Badgett, Majors J; Boyes, Barry; Orlando, Ron

    2018-02-16

    A model that predicts retention for peptides using a HALO ® penta-HILIC column and gradient elution was created. Coefficients for each amino acid were derived using linear regression analysis and these coefficients can be summed to predict the retention of peptides. This model has a high correlation between experimental and predicted retention times (0.946), which is on par with previous RP and HILIC models. External validation of the model was performed using a set of H. pylori samples on the same LC-MS system used to create the model, and the deviation from actual to predicted times was low. Apart from amino acid composition, length and location of amino acid residues on a peptide were examined and two site-specific corrections for hydrophobic residues at the N-terminus as well as hydrophobic residues one spot over from the N-terminus were created. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

    Science.gov (United States)

    Watanabe, Noriya; Sakagami, Masamichi; Haruno, Masahiko

    2013-03-06

    Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

  7. Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

    Directory of Open Access Journals (Sweden)

    Peiying Ruan

    Full Text Available Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

  8. Behavior problems in middle childhood: the predictive role of maternal distress, child attachment, and mother-child interactions.

    Science.gov (United States)

    Dubois-Comtois, Karine; Moss, Ellen; Cyr, Chantal; Pascuzzo, Katherine

    2013-11-01

    The objective of the study was to examine the longitudinal relation between early school-age measures of maternal psychosocial distress, quality of mother-child interactions, and child attachment behavior, and behavior problem profiles in middle childhood using a multi-informant design. Participants were 243 French-speaking mother-child dyads (122 girls) who were part of an ongoing longitudinal project. Maternal psychosocial distress was assessed when children were between 4 and 6 years of age. Mother-child interactive quality and attachment patterns were observed at age 6 during a laboratory visit. At age 8.5, externalizing and internalizing problems were assessed using mother and child reports. Results show that maternal psychosocial distress predicted later social adaptation reported by the child through the mediation of mother-child interactions. Analyses also revealed that higher maternal psychosocial distress and controlling attachment patterns, either of the punitive or caregiving type, significantly predicted membership in both child internalizing and externalizing clinical problem groups. Lower mother-child interactive quality, male gender, and child ambivalent attachment were also predictors of externalizing clinical problems.

  9. War trauma and maternal-fetal attachment predicting maternal mental health, infant development, and dyadic interaction in Palestinian families.

    Science.gov (United States)

    Punamäki, Raija-Leena; Isosävi, Sanna; Qouta, Samir R; Kuittinen, Saija; Diab, Safwat Y

    2017-10-01

    Optimal maternal-fetal attachment (MFA) is believed to be beneficial for infant well-being and dyadic interaction, but research is scarce in general and among risk populations. Our study involved dyads living in war conditions and examined how traumatic war trauma associates with MFA and which factors mediate that association. It also modeled the role of MFA in predicting newborn health, infant development, mother-infant interaction, and maternal postpartum mental health. Palestinian women from the Gaza Strip (N = 511) participated during their second trimester (T1), and when their infants were 4 (T2) and 12 (T3) months. Mothers reported MFA (interaction with, attributions to, and fantasies about the fetus), social support, and prenatal mental health (post-traumatic stress disorder, depression, and anxiety) at T1, newborn health at T2, and the postpartum mental health, infant's sensorimotor and language development, and mother-infant interaction (emotional availability) at T3. Results revealed, first, that war trauma was not directly associated with MFA but that it was mediated through a low level of social support and high level of maternal prenatal mental health problems. Second, intensive MFA predicted optimal mother-reported infant's sensorimotor and language development and mother-infant emotional availability but not newborn health or maternal postpartum mental health.

  10. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

    Directory of Open Access Journals (Sweden)

    Yanxiong Gan

    2015-11-01

    Full Text Available Curcumin, the medically active component from Curcuma longa (Turmeric, is widely used to treat inflammatory diseases. Protein interaction network (PIN analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein–protein interactions (PPIs were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO enrichment analysis based on molecular complex detection (MCODE. A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation.

  11. An analytically-based method for predicting the noise generated by the interaction between turbulence and a serrated leading edge

    Science.gov (United States)

    Mathews, J. R.; Peake, N.

    2018-05-01

    This paper considers the interaction of turbulence with a serrated leading edge. We investigate the noise produced by an aerofoil moving through a turbulent perturbation to uniform flow by considering the scattered pressure from the leading edge. We model the aerofoil as an infinite half plane with a leading edge serration, and develop an analytical model using a Green's function based upon the work of Howe. This allows us to consider both deterministic eddies and synthetic turbulence interacting with the leading edge. We show that it is possible to reduce the noise by using a serrated leading edge compared with a straight edge, but the optimal noise-reducing choice of serration is hard to predict due to the complex interaction. We also consider the effect of angle of attack, and find that in general the serrations are less effective at higher angles of attack.

  12. Covariant Spectator Theory of heavy–light and heavy mesons and the predictive power of covariant interaction kernels

    Energy Technology Data Exchange (ETDEWEB)

    Leitão, Sofia, E-mail: sofia.leitao@tecnico.ulisboa.pt [CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Stadler, Alfred, E-mail: stadler@uevora.pt [Departamento de Física, Universidade de Évora, 7000-671 Évora (Portugal); CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Peña, M.T., E-mail: teresa.pena@tecnico.ulisboa.pt [Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Biernat, Elmar P., E-mail: elmar.biernat@tecnico.ulisboa.pt [CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal)

    2017-01-10

    The Covariant Spectator Theory (CST) is used to calculate the mass spectrum and vertex functions of heavy–light and heavy mesons in Minkowski space. The covariant kernel contains Lorentz scalar, pseudoscalar, and vector contributions. The numerical calculations are performed in momentum space, where special care is taken to treat the strong singularities present in the confining kernel. The observed meson spectrum is very well reproduced after fitting a small number of model parameters. Remarkably, a fit to a few pseudoscalar meson states only, which are insensitive to spin–orbit and tensor forces and do not allow to separate the spin–spin from the central interaction, leads to essentially the same model parameters as a more general fit. This demonstrates that the covariance of the chosen interaction kernel is responsible for the very accurate prediction of the spin-dependent quark–antiquark interactions.

  13. Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail; Soufan, Othman; Essack, Magbubah; Kalnis, Panos; Bajic, Vladimir B.

    2016-01-01

    Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational

  14. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo; Alanis Lobato, Gregorio; Ravasi, Timothy

    2013-01-01

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness

  15. Complex tritrophic interactions in response to crop domestication: predictions from the wild

    NARCIS (Netherlands)

    Chen, Y.H.; Gols, R.; Stratton, C.A.; Brevik, K.A.; Benrey, B.

    2015-01-01

    Crop domestication is the process of artificially selecting plants to increase their suitability to human tastes and cultivated growing conditions. There is increasing evidence that crop domestication can profoundly alter interactions among plants, herbivores, and their natural enemies. However,

  16. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    -target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all

  17. Holistic Approach to Partial Covalent Interactions in Protein Structure Prediction and Design with Rosetta.

    Science.gov (United States)

    Combs, Steven A; Mueller, Benjamin K; Meiler, Jens

    2018-05-29

    Partial covalent interactions (PCIs) in proteins, which include hydrogen bonds, salt bridges, cation-π, and π-π interactions, contribute to thermodynamic stability and facilitate interactions with other biomolecules. Several score functions have been developed within the Rosetta protein modeling framework that identify and evaluate these PCIs through analyzing the geometry between participating atoms. However, we hypothesize that PCIs can be unified through a simplified electron orbital representation. To test this hypothesis, we have introduced orbital based chemical descriptors for PCIs into Rosetta, called the PCI score function. Optimal geometries for the PCIs are derived from a statistical analysis of high-quality protein structures obtained from the Protein Data Bank (PDB), and the relative orientation of electron deficient hydrogen atoms and electron-rich lone pair or π orbitals are evaluated. We demonstrate that nativelike geometries of hydrogen bonds, salt bridges, cation-π, and π-π interactions are recapitulated during minimization of protein conformation. The packing density of tested protein structures increased from the standard score function from 0.62 to 0.64, closer to the native value of 0.70. Overall, rotamer recovery improved when using the PCI score function (75%) as compared to the standard Rosetta score function (74%). The PCI score function represents an improvement over the standard Rosetta score function for protein model scoring; in addition, it provides a platform for future directions in the analysis of small molecule to protein interactions, which depend on partial covalent interactions.

  18. Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

    Science.gov (United States)

    Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

    2018-03-01

    Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

  19. Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

    Science.gov (United States)

    Mueller, Silke C; Drewelow, Bernd

    2013-05-01

    The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam. A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction. A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

  20. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

    Science.gov (United States)

    Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

    2018-06-01

    The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  1. Predicting the sensitivity of populations from individual exposure to chemicals: the role of ecological interactions.

    Science.gov (United States)

    Gabsi, Faten; Schäffer, Andreas; Preuss, Thomas G

    2014-07-01

    Population responses to chemical stress exposure are influenced by nonchemical, environmental processes such as species interactions. A realistic quantification of chemical toxicity to populations calls for the use of methodologies that integrate these multiple stress effects. The authors used an individual-based model for Daphnia magna as a virtual laboratory to determine the influence of ecological interactions on population sensitivity to chemicals with different modes of action on individuals. In the model, hypothetical chemical toxicity targeted different vital individual-level processes: reproduction, survival, feeding rate, or somatic growth rate. As for species interactions, predatory and competition effects on daphnid populations were implemented following a worst-case approach. The population abundance was simulated at different food levels and exposure scenarios, assuming exposure to chemical stress solely or in combination with either competition or predation. The chemical always targeted one vital endpoint. Equal toxicity-inhibition levels differently affected the population abundance with and without species interactions. In addition, population responses to chemicals were highly sensitive to the environmental stressor (predator or competitor) and to the food level. Results show that population resilience cannot be attributed to chemical stress only. Accounting for the relevant ecological interactions would reduce uncertainties when extrapolating effects of chemicals from individuals to the population level. Validated population models should be used for a more realistic risk assessment of chemicals. © 2014 SETAC.

  2. DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

    KAUST Repository

    Olayan, Rawan S.

    2017-11-23

    Motivation Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using five repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new, and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.

  3. Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR.

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2009-12-01

    Full Text Available Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses. The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5alpha-androstan-3beta-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

  4. Interaction of Instrumental and Goal-Directed Learning Modulates Prediction Error Representations in the Ventral Striatum.

    Science.gov (United States)

    Guo, Rong; Böhmer, Wendelin; Hebart, Martin; Chien, Samson; Sommer, Tobias; Obermayer, Klaus; Gläscher, Jan

    2016-12-14

    Goal-directed and instrumental learning are both important controllers of human behavior. Learning about which stimulus event occurs in the environment and the reward associated with them allows humans to seek out the most valuable stimulus and move through the environment in a goal-directed manner. Stimulus-response associations are characteristic of instrumental learning, whereas response-outcome associations are the hallmark of goal-directed learning. Here we provide behavioral, computational, and neuroimaging results from a novel task in which stimulus-response and response-outcome associations are learned simultaneously but dominate behavior at different stages of the experiment. We found that prediction error representations in the ventral striatum depend on which type of learning dominates. Furthermore, the amygdala tracks the time-dependent weighting of stimulus-response versus response-outcome learning. Our findings suggest that the goal-directed and instrumental controllers dynamically engage the ventral striatum in representing prediction errors whenever one of them is dominating choice behavior. Converging evidence in human neuroimaging studies has shown that the reward prediction errors are correlated with activity in the ventral striatum. Our results demonstrate that this region is simultaneously correlated with a stimulus prediction error. Furthermore, the learning system that is currently dominating behavioral choice dynamically engages the ventral striatum for computing its prediction errors. This demonstrates that the prediction error representations are highly dynamic and influenced by various experimental context. This finding points to a general role of the ventral striatum in detecting expectancy violations and encoding error signals regardless of the specific nature of the reinforcer itself. Copyright © 2016 the authors 0270-6474/16/3612650-11$15.00/0.

  5. A computational method to predict fluid-structure interaction of pressure relief valves

    Energy Technology Data Exchange (ETDEWEB)

    Kang, S. K.; Lee, D. H.; Park, S. K.; Hong, S. R. [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    2004-07-01

    An effective CFD (Computational fluid dynamics) method to predict important performance parameters, such as blowdown and chattering, for pressure relief valves in NPPs is provided in the present study. To calculate the valve motion, 6DOF (six degree of freedom) model is used. A chimera overset grid method is utilized to this study for the elimination of grid remeshing problem, when the disk moves. Further, CFD-Fastran which is developed by CFD-RC for compressible flow analysis is applied to an 1' safety valve. The prediction results ensure the applicability of the presented method in this study.

  6. Nucleon-nucleon interaction with quark exchange and prediction of the color van der Waals potential

    International Nuclear Information System (INIS)

    Osman, A.

    1988-01-01

    The nucleon-nucleon interaction is considered by including the color nucleon clusters. The nucleon-nucleon system is treated as a six-quark system. The obtained local potentials reduce the short-range repulsion. The resulting nucleon-nucleon potential, using a quark-quark potential, agress well with the central-force potentials. The phase shifts calculated by using these local potentials are in good agreement with those obtained from other methods. Introducing the quark-quark potential in the nucleon-nucleon interaction leads to a color van der Waals potential much stronger than that implied by experiments

  7. Can toxicokinetic and toxicodynamic modeling be used to understand and predict synergistic interactions between chemicals?

    DEFF Research Database (Denmark)

    Cedergreen, Nina; Dalhoff, Kristoffer; Li, Dan

    2017-01-01

    including synergists. The aim of the present study is to develop a mechanistic toxicokinetic (TK) and toxicodynamic (TD) model for the synergistic mixture of the azole fungicide, propiconazole (the synergist), and the insecticide, α-cypermethrin, on the mortality of the crustacean Daphnia magna. The study...... by their effect on the biotransformation rate but that this effect could only partly be explained by the effect of the two azoles on cytochrome P450 activity, measured on D. magna in vivo. TKTD models of interacting mixtures seem to be a promising tool to test mechanisms of interactions between chemicals...

  8. Childhood and adult socioeconomic position interact to predict health in mid life in a cohort of British women

    Directory of Open Access Journals (Sweden)

    Daniel Nettle

    2017-06-01

    Full Text Available Background Low childhood socioeconomic position (cSEP is associated with poorer adult health, even after adult socioeconomic position (aSEP is adjusted for. However, whether cSEP and aSEP combine additively or non-additively in predicting adult health is less well studied. Some evidence suggests that the combination of low cSEP and low aSEP is associated with worse health than would be predicted from the sum of their individual effects. Methods Using data from female members of the British National Child Development Study cohort, we developed continuous quantitative measures of aSEP and cSEP, and used these to predict self-rated health at ages 23, 33, and 42. Results Lower aSEP predicted poorer heath at all ages. Lower cSEP predicted poorer health at all ages, even after adjustment for aSEP, but the direct effects of cSEP were substantially weaker than those of aSEP. At age 23, the effects of cSEP and aSEP were additive. At ages 33 and 42, cSEP and aSEP interacted, such that the effects of low aSEP on health were more negative if cSEP had also been low. Conclusions As women age, aSEP and cSEP may affect their health interactively. High cSEP, by providing a good start in life, may be partially protective against later negative impacts of low aSEP. We relate this to the extended ‘silver spoon’ principle recently documented in a non-human species.

  9. Factors of Predicted Learning Disorders and their Interaction with Attentional and Perceptual Training Procedures.

    Science.gov (United States)

    Friar, John T.

    Two factors of predicted learning disorders were investigated: (1) inability to maintain appropriate classroom behavior (BEH), (2) perceptual discrimination deficit (PERC). Three groups of first-graders (BEH, PERC, normal control) were administered measures of impulse control, distractability, auditory discrimination, and visual discrimination.…

  10. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-06-12

    Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

  11. Implicit and explicit drinker identities interactively predict in-the-moment alcohol placebo consumption

    Directory of Open Access Journals (Sweden)

    Daniel Frings

    2016-06-01

    Conclusion: These results suggest that explicit identities may be associated more with those beliefs about drinking that one is aware of than behavioral intention. In addition, explicit identities may not predict behavioral enactment well. Implicit identity shows effects on actual behavior and not behavioral intention. Together this highlights the differential influence of reflective (explicit and impulsive (implicit identity in-the-moment behavior.

  12. Attentional bias toward high-calorie food-cues and trait motor impulsivity interactively predict weight gain

    Directory of Open Access Journals (Sweden)

    Adrian Meule

    2016-05-01

    Full Text Available Strong bottom-up impulses and weak top-down control may interactively lead to overeating and, consequently, weight gain. In the present study, female university freshmen were tested at the start of the first semester and again at the start of the second semester. Attentional bias toward high- or low-calorie food-cues was assessed using a dot-probe paradigm and participants completed the Barratt Impulsiveness Scale . Attentional bias and motor impulsivity interactively predicted change in body mass index: motor impulsivity positively predicted weight gain only when participants showed an attentional bias toward high-calorie food-cues. Attentional and non-planning impulsivity were unrelated to weight change. Results support findings showing that weight gain is prospectively predicted by a combination of weak top-down control (i.e. high impulsivity and strong bottom-up impulses (i.e. high automatic motivational drive toward high-calorie food stimuli. They also highlight the fact that only specific aspects of impulsivity are relevant in eating and weight regulation.

  13. Parental Behaviors during Family Interactions Predict Changes in Depression and Anxiety Symptoms during Adolescence

    Science.gov (United States)

    Schwartz, Orli S.; Dudgeon, Paul; Sheeber, Lisa B.; Yap, Marie B. H.; Simmons, Julian G.; Allen, Nicholas B.

    2012-01-01

    This study investigated the prospective, longitudinal relations between parental behaviors observed during parent-adolescent interactions, and the development of depression and anxiety symptoms in a community-based sample of 194 adolescents. Positive and negative parental behaviors were examined, with negative behaviors operationalized to…

  14. Qualities of Peer Relations on Social Networking Websites: Predictions from Negative Mother-Teen Interactions

    Science.gov (United States)

    Szwedo, David E.; Mikami, Amori Yee; Allen, Joseph P.

    2011-01-01

    This study examined associations between characteristics of teenagers' relationships with their mothers and their later socializing behavior and peer relationship quality online. At age 13, teenagers and their mothers participated in an interaction in which mothers' and adolescents' behavior undermining autonomy and relatedness was observed and…

  15. Do maternal interaction and early language predict phonological awareness in 3- to 4-year-olds?

    NARCIS (Netherlands)

    Silvén, M.; Niemi, P.; Voeten, M.J.M.

    2002-01-01

    The present study reports longitudinal data on how phonological awareness is affected by mother-child interaction and the child's language development. Sixty-six Finnish children were videotaped at 12 and 24 months of age with their mother, during joint play episodes, to assess maternal sensitivity

  16. A Study of the Predictive Relationship between Online Social Presence and ONLE Interaction

    Science.gov (United States)

    Tu, Chih-Hsiung; Yen, Cherng-Jyh; Blocher, J. Michael; Chan, Junn-Yih

    2012-01-01

    Open Network Learning Environments (ONLE) are online networks that afford learners the opportunity to participate in creative content endeavors, personalized identity projections, networked mechanism management, and effective collaborative community integration by applying Web 2.0 tools in open environments. It supports social interaction by…

  17. Predicting wind farm wake interaction with RANS: an investigation of the Coriolis force

    DEFF Research Database (Denmark)

    van der Laan, Paul; Hansen, Kurt Schaldemose; Sørensen, Niels N.

    2015-01-01

    A Reynolds-averaged Navier-Stokes code is used to simulate the interaction of two neighboring wind farms. The influence of the Coriolis force is investigated by modeling the atmospheric surface/boundary layer with three different methodologies. The results show that the Coriolis force is negligible...

  18. Gender, Parental Education, and Ability: Their Interacting Roles in Predicting GCSE Success

    Science.gov (United States)

    Glaesser, Judith; Cooper, Barry

    2012-01-01

    We investigate the relations between gender, parental education, ability, and educational achievement in Britain, focusing on the way in which gender and parental education interact with ability to contribute to a pupil's obtaining secondary school qualifications. This allows us to provide evidence relevant to claims concerning the effects of…

  19. Prediction for neutrino-electron cross-sections in Weinberg's model for weak interactions

    NARCIS (Netherlands)

    Hooft, G. 't

    1971-01-01

    Weinberg's theory of purely leptonic weak interactions can be tested in neutrino-electron scattering experiments. Cross-sections must be measured as a function of the energy of the recoil electron. If Weinberg's theory is correct, then the masses of the intermediate vector bosons can be derived from

  20. Comparison of Experimental Models for Predicting Laser Tissue Interaction from 3.8-Micron Lasers

    National Research Council Canada - National Science Library

    Williams, Charles Melville

    2004-01-01

    The purpose of this study was to compare and contrast the effects of single 3.8-micron laser pulses in an in-vitro and in-vivo model of human skin and to demonstrate the efficacy of in-vitro laser tissue interaction models...

  1. Predicting Trophic Interactions and Habitat Utilization in the California Current Ecosystem

    Science.gov (United States)

    2015-09-30

    Foraging patterns: model-data comparison . Simulated (colored circles) and observed (black circles) foraging locations for male sea lion individuals off...focusing on trophic interactions affecting habitat utilization and foraging patterns of California sea lions (CSL) in the California Current Large Marine...by considering patterns of covariability between environmental variables (e.g., temperature, primary production) and foraging patterns and success of

  2. Genetic Vulnerability Interacts with Parenting and Early Care and Education to Predict Increasing Externalizing Behavior

    Science.gov (United States)

    Lipscomb, Shannon T.; Laurent, Heidemarie; Neiderhiser, Jenae M.; Shaw, Daniel S.; Natsuaki, Misaki N.; Reiss, David; Leve, Leslie D.

    2014-01-01

    The current study examined interactions among genetic influences and children's early environments on the development of externalizing behaviors from 18 months to 6 years of age. Participants included 233 families linked through adoption (birth parents and adoptive families). Genetic influences were assessed by birth parent temperamental…

  3. Unique contributions of emotion regulation and executive functions in predicting the quality of parent-child interaction behaviors.

    Science.gov (United States)

    Shaffer, Anne; Obradović, Jelena

    2017-03-01

    Parenting is a cognitive, emotional, and behavioral endeavor, yet limited research investigates parents' executive functions and emotion regulation as predictors of how parents interact with their children. The current study is a multimethod investigation of parental self-regulation in relation to the quality of parenting behavior and parent-child interactions in a diverse sample of parents and kindergarten-age children. Using path analyses, we tested how parent executive functions (inhibitory control) and lack of emotion regulation strategies uniquely relate to both sensitive/responsive behaviors and positive/collaborative behaviors during observed interaction tasks. In our analyses, we accounted for parent education, financial stress, and social support as socioeconomic factors that likely relate to parent executive function and emotion regulation skills. In a diverse sample of primary caregivers (N = 102), we found that direct assessment of parent inhibitory control was positively associated with sensitive/responsive behaviors, whereas parent self-reported difficulties in using emotion regulation strategies were associated with lower levels of positive and collaborative dyadic behaviors. Parent education and financial stress predicted inhibitory control, and social support predicted emotion regulation difficulties; parent education was also a significant predictor of sensitive/responsive behaviors. Greater inhibitory control skills and fewer difficulties identifying effective emotion regulation strategies were not significantly related in our final path model. We discuss our findings in the context of current and emerging parenting interventions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  4. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

    Science.gov (United States)

    Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

    2016-11-01

    Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

  5. Better estimation of protein-DNA interaction parameters improve prediction of functional sites

    Directory of Open Access Journals (Sweden)

    O'Flanagan Ruadhan A

    2008-12-01

    Full Text Available Abstract Background Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment method designed to produce many such sequences. Results We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP, and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites. Conclusion We think that training binding site detection

  6. Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency.

    Science.gov (United States)

    Hisler, Garrett C; Phillips, Alison L; Krizan, Zlatan

    2017-06-01

    Diurnal preference (and chronotype more generally) has been implicated in exercise behavior, but this relation has not been examined using objective exercise measurements nor have potential psychosocial mediators been examined. Furthermore, time-of-day often moderates diurnal preference's influence on outcomes, and it is unknown whether time-of-exercise may influence the relation between chronotype and exercise frequency. The current study examined whether individual differences in diurnal preference ("morningness-eveningness") predict unique variance in exercise frequency and if commonly studied psychosocial variables mediate this relation (i.e., behavioral intentions, internal exercise control, external exercise control, and conscientiousness). Moreover, the study sought to test whether individuals' typical time-of-exercise moderated the impact of diurnal preference on exercise frequency. One hundred twelve healthy adults (mean age = 25.4; SD = 11.6 years) completed baseline demographics and then wore Fitbit Zips® for 4 weeks to objectively measure exercise frequency and typical time-of-exercise. At the end of the study, participants also self-reported recent exercise. Diurnal preference predicted both self-reported exercise and Fitbit-recorded exercise frequency. When evaluating mediators, only conscientiousness emerged as a partial mediator of the relation between diurnal preference and self-reported exercise. In addition, time-of-exercise moderated diurnal preference's relation to both self-reported exercise and Fitbit-recorded exercise frequency such that diurnal preference predicted higher exercise frequency when exercise occurred at a time that was congruent with one's diurnal preference. Based on these findings, diurnal preference is valuable, above and beyond other psychological constructs, in predicting exercise frequency and represents an important variable to incorporate into interventions seeking to increase exercise.

  7. Prediction of drug-packaging interactions via molecular dynamics (MD) simulations.

    Science.gov (United States)

    Feenstra, Peter; Brunsteiner, Michael; Khinast, Johannes

    2012-07-15

    The interaction between packaging materials and drug products is an important issue for the pharmaceutical industry, since during manufacturing, processing and storage a drug product is continuously exposed to various packaging materials. The experimental investigation of a great variety of different packaging material-drug product combinations in terms of efficacy and safety can be a costly and time-consuming task. In our work we used molecular dynamics (MD) simulations in order to evaluate the applicability of such methods to pre-screening of the packaging material-solute compatibility. The solvation free energy and the free energy of adsorption of diverse solute/solvent/solid systems were estimated. The results of our simulations agree with experimental values previously published in the literature, which indicates that the methods in question can be used to semi-quantitatively reproduce the solid-liquid interactions of the investigated systems. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

    Science.gov (United States)

    Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

    2017-01-01

    Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Unique contributions of dynamic versus global measures of parent-child interaction quality in predicting school adjustment.

    Science.gov (United States)

    Bardack, Sarah; Herbers, Janette E; Obradović, Jelena

    2017-09-01

    This study investigates the unique contribution of microsocial and global measures of parent-child positive coregulation (PCR) in predicting children's behavioral and social adjustment in school. Using a community sample of 102 children, ages 4-6, and their parents, we conducted nested path analytic models to identify the unique effects of 2 measures of PCR on school outcomes. Microsocial PCR independently predicted fewer externalizing and inattention/impulsive behaviors in school. Global PCR did not uniquely relate to children's behavioral and social adjustment outcomes. Household socioeconomic status was related to both microsocial and global measures of PCR, but not directly associated with school outcomes. Findings illustrate the importance of using dynamic measures of PCR based on microsocial coding to further understand how the quality of parent-child interaction is related to children's self-regulatory and social development during school transition. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  10. A model for predicting pellet-cladding interaction induced fuel rod failure, based on nonlinear fracture mechanics

    International Nuclear Information System (INIS)

    Jernkvist, L.O.

    1993-01-01

    A model for predicting pellet-cladding mechanical interaction induced fuel rod failure, suitable for implementation in finite element fuel-performance codes, is presented. Cladding failure is predicted by explicitly modelling the propagation of radial cracks under varying load conditions. Propagation is assumed to be due to either iodine induced stress corrosion cracking or ductile fracture. Nonlinear fracture mechanics concepts are utilized in modelling these two mechanisms of crack growth. The novelty of this approach is that the development of cracks, which may ultimately lead to fuel rod failure, can be treated as a dynamic and time-dependent process. The influence of cyclic loading, ramp rates and material creep on the failure mechanism can thereby be investigated. Results of numerical calculations, in which the failure model has been used to study the dependence of cladding creep rate on crack propagation velocity, are presented. (author)

  11. Qualities of Peer Relations on Social Networking Websites: Predictions from Negative Mother-Teen Interactions

    OpenAIRE

    Szwedo, David E.; Mikami, Amori Yee; Allen, Joseph P.

    2011-01-01

    This study examined associations between characteristics of teenagers’ relationships with their mothers and their later socializing behavior and peer relationship quality online. At age 13, teenagers and their mothers participated in an interaction in which mothers’ and adolescents’ behavior undermining autonomy and relatedness was observed, and indicators of teens’ depressive symptoms and social anxiety were assessed. At age 20, youth self-reported on their online behaviors, youths’ social n...

  12. Situational Motivation and Perceived Intensity: Their Interaction in Predicting Changes in Positive Affect from Physical Activity

    OpenAIRE

    Eva Guérin; Michelle S. Fortier

    2012-01-01

    There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE)] i...

  13. Predicting Internalizing Problems in Chinese Children: the Unique and Interactive Effects of Parenting and Child Temperament

    OpenAIRE

    Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun

    2013-01-01

    The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children’s internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (6 – 9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children’s internalizing ...

  14. Predicting Protein-Protein Interactions Using BiGGER: Case Studies

    Directory of Open Access Journals (Sweden)

    Rui M. Almeida

    2016-08-01

    Full Text Available The importance of understanding interactomes makes preeminent the study of protein interactions and protein complexes. Traditionally, protein interactions have been elucidated by experimental methods or, with lower impact, by simulation with protein docking algorithms. This article describes features and applications of the BiGGER docking algorithm, which stands at the interface of these two approaches. BiGGER is a user-friendly docking algorithm that was specifically designed to incorporate experimental data at different stages of the simulation, to either guide the search for correct structures or help evaluate the results, in order to combine the reliability of hard data with the convenience of simulations. Herein, the applications of BiGGER are described by illustrative applications divided in three Case Studies: (Case Study A in which no specific contact data is available; (Case Study B when different experimental data (e.g., site-directed mutagenesis, properties of the complex, NMR chemical shift perturbation mapping, electron tunneling on one of the partners is available; and (Case Study C when experimental data are available for both interacting surfaces, which are used during the search and/or evaluation stage of the docking. This algorithm has been extensively used, evidencing its usefulness in a wide range of different biological research fields.

  15. Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

    Directory of Open Access Journals (Sweden)

    Rui Xue

    2015-01-01

    Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

  16. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

    KAUST Repository

    Kulmanov, Maxat

    2017-09-27

    Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

  17. Stress sensitivity interacts with depression history to predict depressive symptoms among youth: Prospective changes following first depression onset

    Science.gov (United States)

    Technow, Jessica R.; Hazel, Nicholas A.; Abela, John R. Z.; Hankin, Benjamin L.

    2015-01-01

    Predictors of depressive symptoms may differ before and after the first onset of major depression due to stress sensitization. Dependent stressors, or those to which characteristics of individuals contribute, have been shown to predict depressive symptoms in youth. The current study sought to clarify how stressors’ roles may differ before and after the first depressive episode. Adolescents (N = 382, aged 11 to 15 at baseline) were assessed at baseline and every three months over the course of two years with measures of stressors and depressive symptoms. Semi-structured interviews were conducted every 6 months to assess for clinically significant depressive episodes. Hierarchical linear modeling showed a significant interaction between history of depression and idiographic fluctuations in dependent stressors to predict prospective elevations of symptoms, such that dependent stressors were more predictive of depressive symptoms after onset of disorder. Independent stressors predicted symptoms, but the strength of the association did not vary by depression history. These results suggest a synthesis of stress sensitization and generation processes that might maintain inter-episode depressive symptoms among youth with a history of clinical depression. PMID:25123081

  18. Stress sensitivity interacts with depression history to predict depressive symptoms among youth: prospective changes following first depression onset.

    Science.gov (United States)

    Technow, Jessica R; Hazel, Nicholas A; Abela, John R Z; Hankin, Benjamin L

    2015-04-01

    Predictors of depressive symptoms may differ before and after the first onset of major depression due to stress sensitization. Dependent stressors, or those to which characteristics of individuals contribute, have been shown to predict depressive symptoms in youth. The current study sought to clarify how stressors' roles may differ before and after the first depressive episode. Adolescents (N = 382, aged 11 to 15 at baseline) were assessed at baseline and every 3 months over the course of 2 years with measures of stressors and depressive symptoms. Semi-structured interviews were conducted every 6 months to assess for clinically significant depressive episodes. Hierarchical linear modeling showed a significant interaction between history of depression and idiographic fluctuations in dependent stressors to predict prospective elevations of symptoms, such that dependent stressors were more predictive of depressive symptoms after onset of disorder. Independent stressors predicted symptoms, but the strength of the association did not vary by depression history. These results suggest a synthesis of dependent stress and stress sensitization processes that might maintain inter-episode depressive symptoms among youth with a history of clinical depression.

  19. Interaction of CD38 Variant and Chronic Interpersonal Stress Prospectively Predicts Social Anxiety and Depression Symptoms Over Six Years

    Science.gov (United States)

    Tabak, Benjamin A.; Vrshek-Schallhorn, Suzanne; Zinbarg, Richard E.; Prenoveau, Jason M.; Mineka, Susan; Redei, Eva E.; Adam, Emma K.; Craske, Michelle G.

    2015-01-01

    Variation in the CD38 gene, which regulates secretion of the neuropeptide oxytocin, has been associated with several social phenotypes. Specifically, rs3796863 A allele carriers have demonstrated increased social sensitivity. In 400 older adolescents, we used trait-state-occasion modeling to investigate how rs3796863 genotype, baseline ratings of chronic interpersonal stress, and their gene-environment (GxE) interaction predicted trait social anxiety and depression symptoms over six years. We found significant GxE effects for CD38 A-carrier genotypes and chronic interpersonal stress at baseline predicting greater social anxiety and depression symptoms. A significant GxE effect of smaller magnitude was also found for C/C genotype and chronic interpersonal stress predicting greater depression; however, this effect was small compared to the main effect of chronic interpersonal stress. Thus, in the context of chronic interpersonal stress, heightened social sensitivity associated with the rs3796863 A allele may prospectively predict risk for social anxiety and (to a lesser extent) depression. PMID:26958455

  20. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

    Science.gov (United States)

    Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

    2018-02-15

    A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  1. CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

    Science.gov (United States)

    Kostal, Jakub; Voutchkova-Kostal, Adelina

    2016-01-19

    Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

  2. The additive and interactive effects of parenting and temperament in predicting adjustment problems of children of divorce.

    Science.gov (United States)

    Lengua, L J; Wolchik, S A; Sandler, I N; West, S G

    2000-06-01

    Investigated the interaction between parenting and temperament in predicting adjustment problems in children of divorce. The study utilized a sample of 231 mothers and children, 9 to 12 years old, who had experienced divorce within the previous 2 years. Both mothers' and children's reports on parenting, temperament, and adjustment variables were obtained and combined to create cross-reporter measures of the variables. Parenting and temperament were directly and independently related to outcomes consistent with an additive model of their effects. Significant interactions indicated that parental rejection was more strongly related to adjustment problems for children low in positive emotionality, and inconsistent discipline was more strongly related to adjustment problems for children high in impulsivity. These findings suggest that children who are high in impulsivity may be at greater risk for developing problems, whereas positive emotionality may operate as a protective factor, decreasing the risk of adjustment problems in response to negative parenting.

  3. Experimental prediction of tube support interaction characteristics in steam generators: Volume 2, Westinghouse Model 51 flow entrance region: Topical report

    International Nuclear Information System (INIS)

    Haslinger, K.H.

    1988-06-01

    Tube-to-tube support interaction characterisitics were determined experimentally on a single tube, multi-span geometry, representative of the Westinghouse Model 51 steam generator economizer design. Results, in part, became input for an autoclave type wear test program on steam generator tubes, performed by Kraftwerk Union (KWU). More importantly, the test data reported here have been used to validate two analytical wear prediction codes; the WECAN code, which was developed by Westinghouse, and the ABAQUS code which has been enhanced for EPRI by Foster Wheeler to enable simulation of gap conditions (including fluid film effects) for various support geometries

  4. Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels

    Directory of Open Access Journals (Sweden)

    McDermott Drew

    2009-08-01

    Full Text Available Abstract Background Proteins interact through specific binding interfaces that contain many residues in domains. Protein interactions thus occur on three different levels of a concept hierarchy: whole-proteins, domains, and residues. Each level offers a distinct and complementary set of features for computationally predicting interactions, including functional genomic features of whole proteins, evolutionary features of domain families and physical-chemical features of individual residues. The predictions at each level could benefit from using the features at all three levels. However, it is not trivial as the features are provided at different granularity. Results To link up the predictions at the three levels, we propose a multi-level machine-learning framework that allows for explicit information flow between the levels. We demonstrate, using representative yeast interaction networks, that our algorithm is able to utilize complementary feature sets to make more accurate predictions at the three levels than when the three problems are approached independently. To facilitate application of our multi-level learning framework, we discuss three key aspects of multi-level learning and the corresponding design choices that we have made in the implementation of a concrete learning algorithm. 1 Architecture of information flow: we show the greater flexibility of bidirectional flow over independent levels and unidirectional flow; 2 Coupling mechanism of the different levels: We show how this can be accomplished via augmenting the training sets at each level, and discuss the prevention of error propagation between different levels by means of soft coupling; 3 Sparseness of data: We show that the multi-level framework compounds data sparsity issues, and discuss how this can be dealt with by building local models in information-rich parts of the data. Our proof-of-concept learning algorithm demonstrates the advantage of combining levels, and opens up

  5. Predicting anxious response to a social challenge: the predictive utility of the social interaction anxiety scale and the social phobia scale in a college population.

    Science.gov (United States)

    Gore, K L; Carter, M M; Parker, S

    2002-06-01

    Trait anxiety is believed to be a hierarchical construct composed of several lower-order factors (Adv. Behav. Res. Therapy, 15 (1993) 147; J. Anxiety Disorders, 9 (1995) 163). Assessment devices such as the Social Interaction Anxiety Scale, the Social Phobia Scale (SIAS and SPS; Behav. Res. Therapy, 36 (4) (1998) 455), and the Anxiety Sensitivity Index (ASI; Behav. Res. Therapy, 24 (1986) 1) are good measures of the presumably separate lower-order factors. This study compared the effectiveness of the SIAS, SPS, ASI-physical scale and STAI-T (State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press (1970)) as predictors of anxious response to a social challenge (asking an aloof confederate out on a date). Consistent with the hierarchical model of anxiety, the measures of trait anxiety were moderately correlated with each other and each was a significant predictor of anxious response. The specific measures of trait social anxiety were slightly better predictors of anxious response to the social challenge than was either the ASI-physical scale or the STAI-T. The results provide evidence of the predictive validity of these social trait measures and some support for their specificity in the prediction of anxious response to a social challenge.

  6. Variation in GYS1 interacts with exercise and gender to predict cardiovascular mortality.

    Directory of Open Access Journals (Sweden)

    Jenny Fredriksson

    Full Text Available BACKGROUND: The muscle glycogen synthase gene (GYS1 has been associated with type 2 diabetes (T2D, the metabolic syndrome (MetS, male myocardial infarction and a defective increase in muscle glycogen synthase protein in response to exercise. We addressed the questions whether polymorphism in GYS1 can predict cardiovascular (CV mortality in a high-risk population, if this risk is influenced by gender or physical activity, and if the association is independent of genetic variation in nearby apolipoprotein E gene (APOE. METHODOLOGY/PRINCIPAL FINDINGS: Polymorphisms in GYS1 (XbaIC>T and APOE (-219G>T, epsilon2/epsilon3/epsilon4 were genotyped in 4,654 subjects participating in the Botnia T2D-family study and followed for a median of eight years. Mortality analyses were performed using Cox proportional-hazards regression. During the follow-up period, 749 individuals died, 409 due to CV causes. In males the GYS1 XbaI T-allele (hazard ratio (HR 1.9 [1.2-2.9], T2D (2.5 [1.7-3.8], earlier CV events (1.7 [1.2-2.5], physical inactivity (1.9 [1.2-2.9] and smoking (1.5 [1.0-2.3] predicted CV mortality. The GYS1 XbaI T-allele predicted CV mortality particularly in physically active males (HR 1.7 [1.3-2.0]. Association of GYS1 with CV mortality was independent of APOE (219TT/epsilon4, which by its own exerted an effect on CV mortality risk in females (2.9 [1.9-4.4]. Other independent predictors of CV mortality in females were fasting plasma glucose (1.2 [1.1-1.2], high body mass index (BMI (1.0 [1.0-1.1], hypertension (1.9 [1.2-3.1], earlier CV events (1.9 [1.3-2.8] and physical inactivity (1.9 [1.2-2.8]. CONCLUSIONS/SIGNIFICANCE: Polymorphisms in GYS1 and APOE predict CV mortality in T2D families in a gender-specific fashion and independently of each other. Physical exercise seems to unmask the effect associated with the GYS1 polymorphism, rendering carriers of the variant allele less susceptible to the protective effect of exercise on the risk of CV death

  7. Dopamine D4 Receptor Polymorphism and Sex Interact to Predict Children's Affective Knowledge

    Directory of Open Access Journals (Sweden)

    Sharon eBen-Israel

    2015-06-01

    Full Text Available Affective knowledge, the ability to understand others’ emotional states, is considered to be a fundamental part in efficient social interaction. Affective knowledge can be seen as related to cognitive empathy, and in the framework of Theory of Mind (ToM as affective ToM. Previous studies found that cognitive empathy and ToM are heritable, yet little is known regarding the specific genes involved in individual variability in affective knowledge. Investigating the genetic basis of affective knowledge is important for understanding brain mechanisms underlying socio-cognitive abilities. The 7-repeat (7R allele within the third exon of the Dopamine D4 receptor gene (DRD4-III has been a focus of interest, due to accumulated knowledge regarding its relevance to individual differences in prosocial behavior. A recent study suggests that an interaction between the DRD4-III polymorphism and sex is associated with cognitive empathy among adults. We aimed to examine the same association in two childhood age groups. Children (N = 280, age 3.5 years, N = 283, age 5 years participated as part of the Longitudinal Israel Study of Twins (LIST. Affective knowledge was assessed through children’s responses to an illustrated story describing different emotional situations, told in a laboratory setting. The findings suggest a significant interaction between sex and the DRD4-III polymorphism, replicated in both age groups. Boy carriers of the 7R allele had higher affective knowledge scores than girls, whereas in the absence of the 7R there was no significant sex effect on affective knowledge. The results support the importance of DRD4-III polymorphism and sex differences to social development. Possible explanations for differences from adult findings are discussed, as are pathways for future studies.

  8. The interaction between aggrecan gene VNTR polymorphism and obesity in predicting incident symptomatic lumbar disc herniation.

    Science.gov (United States)

    Cong, Lin; Zhu, Yue; Pang, Hao; Guanjun, T U

    2014-01-01

    An association between aggrecan gene variable number of tandem repeats polymorphism (VNTR) and symptomatic lumbar disc herniation (LDH) has been reported in Chinese Han of Northern China, and obesity had previously been suspected of causing severe LDH. However, the interaction between aggrecan VNTR and obesity in symptomatic LDH has not been well studied. To examine the interaction between aggrecan VNTR and obesity in the susceptibility of symptomatic LDH, 259 participants participated in this study and donated a blood sample. The disease group comprised 61 patients already diagnosed with symptomatic LDH. The control group consisted of 198 healthy blood donors without symptoms of LDH who were not diagnosed with LDH. The aggrecan gene VNTR region was analyzed using polymerase chain reaction. The data indicated that between the two groups, participants carrying one or two alleles ≤25 repeats who were non-obese people showed a 1.057-fold increase in risk for symptomatic LDH (p = 0.895, changing the number of repeat alleles to 25 repeats who were obese people showed an 1.061-fold higher risk (p = 0.885, adding obesity to the mix alone did not demonstrably increase the risk of LDH), while participants carrying one or two alleles ≤25 repeats who were obese people showed a 4.667-fold increase in risk for symptomatic LDH (p = 0.0003, adding obesity plus changing the repeat allele number significantly increased the risk of LDH by 4.667). Overall, the findings suggest an underlying interaction between aggrecan VNTR and obesity in symptomatic LDH.

  9. Prediction of the pressure-time history due to fuel-sodium interaction in a subassembly

    International Nuclear Information System (INIS)

    Jacobs, H.

    1975-01-01

    A local cooling disturbance may lead to complete voiding of a subassembly and melt down of the fuel pins. Thus molten fuel may be accumulated and mixed with liquid sodium returning accidentally into the subassembly. The resulting fuel-sodium interaction (FSI) produces a pressure load on the surrounding core structures. It is necessary to prove that the corresponding core deformation neither initiates a nuclear excursion nor renders the shut down system inoperable. This requires the knowledge of the initiating FSI pressure time history. In this paper a theoretical pressure time history is presented which differs completely from all calculations known so far. (Auth.)

  10. Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans

    Directory of Open Access Journals (Sweden)

    Min JS

    2016-09-01

    Full Text Available Jee Sun Min,1 Doyun Kim,1 Jung Bae Park,1 Hyunjin Heo,1 Soo Hyeon Bae,2 Jae Hong Seo,1 Euichaul Oh,1 Soo Kyung Bae1 1Integrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, 2Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seocho-gu, Seoul, South Korea Background: Evaluating the potential risk of metabolic drug–drug interactions (DDIs is clinically important. Objective: To develop a physiologically based pharmacokinetic (PBPK model for sarpogrelate hydrochloride and its active metabolite, (R,S-1-{2-[2-(3-methoxyphenylethyl]-phenoxy}-3-(dimethylamino-2-propanol (M-1, in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine. Methods: The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol. Results: The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol

  11. INTERACT

    DEFF Research Database (Denmark)

    Jochum, Elizabeth; Borggreen, Gunhild; Murphey, TD

    This paper considers the impact of visual art and performance on robotics and human-computer interaction and outlines a research project that combines puppetry and live performance with robotics. Kinesics—communication through movement—is the foundation of many theatre and performance traditions ...

  12. Computational analysis and prediction of the binding motif and protein interacting partners of the Abl SH3 domain.

    Directory of Open Access Journals (Sweden)

    Tingjun Hou

    2006-01-01

    Full Text Available Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains, such as Src Homology 2 and 3 (SH2 and SH3 domains, which bind to specific sequence and structural motifs. It is important but challenging to determine the binding specificity of these domains accurately and to predict their physiological interacting partners. In this study, the interactions between 35 peptide ligands (15 binders and 20 non-binders and the Abl SH3 domain were analyzed using molecular dynamics simulation and the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. The calculated binding free energies correlated well with the rank order of the binding peptides and clearly distinguished binders from non-binders. Free energy component analysis revealed that the van der Waals interactions dictate the binding strength of peptides, whereas the binding specificity is determined by the electrostatic interaction and the polar contribution of desolvation. The binding motif of the Abl SH3 domain was then determined by a virtual mutagenesis method, which mutates the residue at each position of the template peptide relative to all other 19 amino acids and calculates the binding free energy difference between the template and the mutated peptides using the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. A single position mutation free energy profile was thus established and used as a scoring matrix to search peptides recognized by the Abl SH3 domain in the human genome. Our approach successfully picked ten out of 13 experimentally determined binding partners of the Abl SH3 domain among the top 600 candidates from the 218,540 decapeptides with the PXXP motif in the SWISS-PROT database. We expect that this physical-principle based method can be applied to other protein domains as well.

  13. The Interaction of Temporal and Spectral Acoustic Information with Word Predictability on Speech Intelligibility

    Science.gov (United States)

    Shahsavarani, Somayeh Bahar

    High-level, top-down information such as linguistic knowledge is a salient cortical resource that influences speech perception under most listening conditions. But, are all listeners able to exploit these resources for speech facilitation to the same extent? It was found that children with cochlear implants showed different patterns of benefit from contextual information in speech perception compared with their normal-haring peers. Previous studies have discussed the role of non-acoustic factors such as linguistic and cognitive capabilities to account for this discrepancy. Given the fact that the amount of acoustic information encoded and processed by auditory nerves of listeners with cochlear implants differs from normal-hearing listeners and even varies across individuals with cochlear implants, it is important to study the interaction of specific acoustic properties of the speech signal with contextual cues. This relationship has been mostly neglected in previous research. In this dissertation, we aimed to explore how different acoustic dimensions interact to affect listeners' abilities to combine top-down information with bottom-up information in speech perception beyond the known effects of linguistic and cognitive capacities shown previously. Specifically, the present study investigated whether there were any distinct context effects based on the resolution of spectral versus slowly-varying temporal information in perception of spectrally impoverished speech. To that end, two experiments were conducted. In both experiments, a noise-vocoded technique was adopted to generate spectrally-degraded speech to approximate acoustic cues delivered to listeners with cochlear implants. The frequency resolution was manipulated by varying the number of frequency channels. The temporal resolution was manipulated by low-pass filtering of amplitude envelope with varying low-pass cutoff frequencies. The stimuli were presented to normal-hearing native speakers of American

  14. MIEC-SVM: automated pipeline for protein peptide/ligand interaction prediction.

    Science.gov (United States)

    Li, Nan; Ainsworth, Richard I; Wu, Meixin; Ding, Bo; Wang, Wei

    2016-03-15

    MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency. The program is available at http://wanglab.ucsd.edu/MIEC-SVM CONTACT: : wei-wang@ucsd.edu Supplementary data available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  15. Predictions of baryon form factors for the electromagnetic and weak interaction

    International Nuclear Information System (INIS)

    Kiehlmann, H.D.

    1978-05-01

    The electromagnetic and weak form factors of the baryon matrix elements (with B the nucleon or the Λ(1232)-resonance) are determined via sumrules by the experimentally known form factors of the nucleon matrix element for momentum transfers 0 2 2 . The operator Jμ denotes either the electromagnetic current or the weak hypercharge-conserving current of the I. class. The sumrules are derived from the superconvergence of properly chosen reaction amplitudes. The results allow an absolute determination of the cross sections of a series of peripheral reactions. An important and interesting consequence for the considered matrix elements of the weak current is that the properties of CVC of PCAC follow from the sumrules without additional assumptions. Finally the predictions of relativistic SU(6)-models are checked. One gets surprisingly a good confirmation of the essential results of these models, the reliability of which has almost been unknown on account of a series of speculative assumptions. (orig.) [de

  16. The interactive roles of mastery climate and performance climate in predicting intrinsic motivation.

    Science.gov (United States)

    Buch, R; Nerstad, C G L; Säfvenbom, R

    2017-02-01

    This study examined the interplay between perceived mastery and performance climates in predicting increased intrinsic motivation. The results of a two-wave longitudinal study comprising of 141 individuals from three military academies revealed a positive relationship between a perceived mastery climate and increased intrinsic motivation only for individuals who perceived a low performance climate. This finding suggests a positive relationship between a perceived mastery climate and increased intrinsic motivation only when combined with low perceptions of a performance climate. Hence, introducing a performance climate in addition to a mastery climate can be an undermining motivational strategy, as it attenuates the positive relationship between a mastery climate and increased intrinsic motivation. Implications for future research and practice are discussed. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. Predictability of multispecies competitive interactions in three populations of Atlantic salmon Salmo salar.

    Science.gov (United States)

    Houde, A L S; Wilson, C C; Neff, B D

    2015-04-01

    Juvenile Atlantic salmon Salmo salar from three allopatric populations (LaHave, Sebago and Saint-Jean) were placed into artificial streams with combinations of four non-native salmonids: brown trout Salmo trutta, rainbow trout Oncorhynchus mykiss, Chinook salmon Oncorhynchus tshawytscha and coho salmon Oncorhynchus kisutch. Non-additive effects, as evidenced by lower performance than predicted from weighted summed two-species competition trials, were detected for S. salar fork length (LF ) and mass, but not for survival, condition factor or riffle use. These data support emerging theory on niche overlap and species richness as factors that can lead to non-additive competition effects. © 2015 The Fisheries Society of the British Isles.

  18. An accurate model for numerical prediction of piezoelectric energy harvesting from fluid structure interaction problems

    International Nuclear Information System (INIS)

    Amini, Y; Emdad, H; Farid, M

    2014-01-01

    Piezoelectric energy harvesting (PEH) from ambient energy sources, particularly vibrations, has attracted considerable interest throughout the last decade. Since fluid flow has a high energy density, it is one of the best candidates for PEH. Indeed, a piezoelectric energy harvesting process from the fluid flow takes the form of natural three-way coupling of the turbulent fluid flow, the electromechanical effect of the piezoelectric material and the electrical circuit. There are some experimental and numerical studies about piezoelectric energy harvesting from fluid flow in literatures. Nevertheless, accurate modeling for predicting characteristics of this three-way coupling has not yet been developed. In the present study, accurate modeling for this triple coupling is developed and validated by experimental results. A new code based on this modeling in an openFOAM platform is developed. (paper)

  19. The interaction of economic rewards and moral convictions in predicting attitudes toward resource use.

    Science.gov (United States)

    Bastian, Brock; Zhang, Airong; Moffat, Kieren

    2015-01-01

    When people are morally convicted regarding a specific issue, these convictions exert a powerful influence on their attitudes and behavior. In the current research we examined whether there are boundary conditions to the influence of this effect. Specifically, whether in the context of salient economic rewards, moral convictions may become weaker predictors of attitudes regarding resource use. Focusing on the issue of mining we gathered large-scale samples across three different continents (Australia, Chile, and China). We found that moral convictions against mining were related to a reduced acceptance of mining in each country, while perceived economic rewards from mining increased acceptance. These two motivations interacted, however, such that when perceived economic benefit from mining was high, the influence of moral conviction was weaker. The results highlight the importance of understanding the roles of both moral conviction and financial gain in motivating attitudes towards resource use.

  20. The interaction of economic rewards and moral convictions in predicting attitudes toward resource use.

    Directory of Open Access Journals (Sweden)

    Brock Bastian

    Full Text Available When people are morally convicted regarding a specific issue, these convictions exert a powerful influence on their attitudes and behavior. In the current research we examined whether there are boundary conditions to the influence of this effect. Specifically, whether in the context of salient economic rewards, moral convictions may become weaker predictors of attitudes regarding resource use. Focusing on the issue of mining we gathered large-scale samples across three different continents (Australia, Chile, and China. We found that moral convictions against mining were related to a reduced acceptance of mining in each country, while perceived economic rewards from mining increased acceptance. These two motivations interacted, however, such that when perceived economic benefit from mining was high, the influence of moral conviction was weaker. The results highlight the importance of understanding the roles of both moral conviction and financial gain in motivating attitudes towards resource use.

  1. IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein-Ligand Interactions.

    Science.gov (United States)

    Da Silva, Franck; Desaphy, Jeremy; Rognan, Didier

    2018-03-20

    Structure-based ligand design requires an exact description of the topology of molecular entities under scrutiny. IChem is a software package that reflects the many contributions of our research group in this area over the last decade. It facilitates and automates many tasks (e.g., ligand/cofactor atom typing, identification of key water molecules) usually left to the modeler's choice. It therefore permits the detection of molecular interactions between two molecules in a very precise and flexible manner. Moreover, IChem enables the conversion of intricate three-dimensional (3D) molecular objects into simple representations (fingerprints, graphs) that facilitate knowledge acquisition at very high throughput. The toolkit is an ideal companion for setting up and performing many structure-based design computations. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  2. Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.

    Science.gov (United States)

    Xu, Weijia; Ozer, Stuart; Gutell, Robin R

    2009-01-01

    With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure.

  3. Prediction of protein-protein interactions in dengue virus coat proteins guided by low resolution cryoEM structures

    Directory of Open Access Journals (Sweden)

    Srinivasan Narayanaswamy

    2010-06-01

    Full Text Available Abstract Background Dengue virus along with the other members of the flaviviridae family has reemerged as deadly human pathogens. Understanding the mechanistic details of these infections can be highly rewarding in developing effective antivirals. During maturation of the virus inside the host cell, the coat proteins E and M undergo conformational changes, altering the morphology of the viral coat. However, due to low resolution nature of the available 3-D structures of viral assemblies, the atomic details of these changes are still elusive. Results In the present analysis, starting from Cα positions of low resolution cryo electron microscopic structures the residue level details of protein-protein interaction interfaces of dengue virus coat proteins have been predicted. By comparing the preexisting structures of virus in different phases of life cycle, the changes taking place in these predicted protein-protein interaction interfaces were followed as a function of maturation process of the virus. Besides changing the current notion about the presence of only homodimers in the mature viral coat, the present analysis indicated presence of a proline-rich motif at the protein-protein interaction interface of the coat protein. Investigating the conservation status of these seemingly functionally crucial residues across other members of flaviviridae family enabled dissecting common mechanisms used for infections by these viruses. Conclusions Thus, using computational approach the present analysis has provided better insights into the preexisting low resolution structures of virus assemblies, the findings of which can be made use of in designing effective antivirals against these deadly human pathogens.

  4. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

    Directory of Open Access Journals (Sweden)

    Xuan Xiao

    Full Text Available Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

  5. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

    Science.gov (United States)

    Ding, Fangrui; Tan, Aidi; Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

    2016-01-01

    Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

  6. Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

    Science.gov (United States)

    Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

    2018-05-07

    Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

  7. Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents.

    Science.gov (United States)

    Collins, Michael G; Juvina, Ion; Gluck, Kevin A

    2016-01-01

    When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair.

  8. Negative (but not Positive) Parenting Interacts with Infant Negative Affect to Predict Infant Approach: Evidence of Diathesis-Stress.

    Science.gov (United States)

    Holzman, Jacob B; Burt, Nicole M; Edwards, Erin S; Rosinski, Leanna D; Bridgett, David J

    2018-01-01

    Temperament by parenting interactions may reflect that individuals with greater risk are more likely to experience negative outcomes in adverse contexts (diathesis-stress) or that these individuals are more susceptible to contextual influences in a 'for better or for worse' pattern (differential susceptibility). Although such interactions have been identified for a variety of child outcomes, prior research has not examined approach characteristics - excitement and approach toward pleasurable activities - in the first year of life. Therefore, the current study investigated whether 6-month maternal reported infant negative affect - a phenotypic marker of risk/susceptibility - interacted with 8-month observed parenting behaviors (positive parenting, negative parenting) to predict 12-month infant behavioral approach. Based a sample of mothers and their infants ( N =150), results indicated that negative parenting was inversely associated with subsequent approach for infants with high, but not low, levels of early negative affect. Similar results did not occur regarding positive parenting. These findings better fit a diathesis-stress model rather than a differential susceptibility model. Implications and limitations of these findings are discussed.

  9. Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model.

    Science.gov (United States)

    Batmanov, Kirill; Wang, Junbai

    2017-09-18

    DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor-DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein-DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor-DNA interaction, as well as for predicting cancer mutation effects in the future.

  10. Heuristics and Biases: Interactions among Numeracy, Ability, and Reflectiveness Predict Normative Responding

    Directory of Open Access Journals (Sweden)

    Paul A Klaczynski

    2014-07-01

    Full Text Available In Stanovich's (2009a, 2011 dual-process theory, analytic processing occurs in the algorithmic and reflective minds. Thinking dispositions, indexes of reflective mind functioning, are believed to regulate operations at the algorithmic level, indexed by general cognitive ability. General limitations at the algorithmic level impose constraints on, and affect the adequacy of, specific strategies and abilities (e.g., numeracy. In a study of 216 undergraduates, the hypothesis that thinking dispositions and general ability moderate the relationship between numeracy (understanding of mathematical concepts and attention to numerical information and normative responses on probabilistic heuristics and biases problems was tested. Although all three individual difference measures predicted normative responses, the numeracy-normative response association depended on thinking dispositions and general ability. Specifically, numeracy directly affected normative responding only at relatively high levels of thinking dispositions and general ability. At low levels of thinking dispositions, neither general ability nor numeric skills related to normative responses. Discussion focuses on the consistency of these findings with the hypothesis that the implementation of specific skills is constrained by limitations at both the reflective level and the algorithmic level, methodological limitations that prohibit definitive conclusions, and alternative explanations.

  11. Heuristics and biases: interactions among numeracy, ability, and reflectiveness predict normative responding.

    Science.gov (United States)

    Klaczynski, Paul A

    2014-01-01

    In Stanovich's (2009a, 2011) dual-process theory, analytic processing occurs in the algorithmic and reflective minds. Thinking dispositions, indexes of reflective mind functioning, are believed to regulate operations at the algorithmic level, indexed by general cognitive ability. General limitations at the algorithmic level impose constraints on, and affect the adequacy of, specific strategies and abilities (e.g., numeracy). In a study of 216 undergraduates, the hypothesis that thinking dispositions and general ability moderate the relationship between numeracy (understanding of mathematical concepts and attention to numerical information) and normative responses on probabilistic heuristics and biases (HB) problems was tested. Although all three individual difference measures predicted normative responses, the numeracy-normative response association depended on thinking dispositions and general ability. Specifically, numeracy directly affected normative responding only at relatively high levels of thinking dispositions and general ability. At low levels of thinking dispositions, neither general ability nor numeric skills related to normative responses. Discussion focuses on the consistency of these findings with the hypothesis that the implementation of specific skills is constrained by limitations at both the reflective level and the algorithmic level, methodological limitations that prohibit definitive conclusions, and alternative explanations.

  12. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  13. Couple Interaction and Predicting Vulnerability to Domestic Violence in Uttar Pradesh, India.

    Science.gov (United States)

    Singh, Brijesh P; Singh, Kaushalendra K; Singh, Neha

    2014-08-01

    Domestic violence, when conducted against women, is a type of gender-based violence that negatively impacts a woman's physical and psychological health, causing insecurity, lack of safety, and loss of health and self-worth. Domestic violence is an important consideration for sexual, reproductive, and child health, as it can affect contraceptive behaviors of couples as well as levels of infant mortality. In the present analysis, an attempt has been made to study the relationship between women's experience of domestic violence and couple interaction after controlling for certain socioeconomic and demographic variables using logistic regression. This study looks at data from the National Family Health Survey-III conducted from 2005 to 2006 in Uttar Pradesh, the most populous state of India. Findings reveal that 43% of women suffer from domestic violence in the society as a whole; however, if a couple makes joint decisions in household matters, the prevalence of domestic violence is observed to be 24% less. Education and occupation of women, standard of living, media exposure, and partner's alcoholic behaviors are also found to be possible predictors of domestic violence. © The Author(s) 2014.

  14. Predicting the effect of interactive video bikes on exercise adherence: An efficacy trial.

    Science.gov (United States)

    Rhodes, Ryan E; Warburton, Darren E R; Bredin, Shannon S D

    2009-12-01

    Exercise games that employ video game technology are increasing in the marketplace but have received scant research attention despite their popularity. The purpose of this study was to evaluate the effect of videobike gaming on the constructs of the theory of planned behavior (TPB) and adherence in comparison to a cycling condition where participants listen to self-selected music. Participants were 29 inactive young men assigned randomly to experimental (n = 16) or comparison (n = 13) conditions. The recommended training regime consisted of moderate intensity activity (60-75% heart rate reserve), 3 days/week for 30 min/day for 6 weeks. At the end of the first session, participants were asked to complete TPB measures and these were subsequently measured 6 weeks later. Attendance was used as the measure of adherence. Results showed that affective attitude and adherence across the 6 weeks significantly favored the videobike condition over the comparison condition. Regression analyses suggested partial mediation of the effect of the videobike condition on adherence via affective attitude. This is the first study to provide evidence that interactive videobikes may improve adherence over traditional cycling because the activity produces higher affective attitudes. The results are promising for expanding to community-based evaluation.

  15. Serotonin Transporter-Linked Polymorphic Region (5-HTTLPR) Genotype and Stressful Life Events Interact to Predict Preschool-Onset Depression: A Replication and Developmental Extension

    Science.gov (United States)

    Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S.; Tillman, Rebecca; Luby, Joan L.

    2014-01-01

    Background: Scientific enthusiasm about gene × environment interactions, spurred by the 5-HTTLPR (serotonin transporter-linked polymorphic region) × SLEs (stressful life events) interaction predicting depression, have recently been tempered by sober realizations of small effects and meta-analyses reaching opposing conclusions. These mixed findings…

  16. Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT.

    Science.gov (United States)

    Zabelina, Darya L; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

    2016-01-01

    The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity--an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately.

  17. Interaction of the ADRB2 gene polymorphism with childhood trauma in predicting adult symptoms of posttraumatic stress disorder.

    Science.gov (United States)

    Liberzon, Israel; King, Anthony P; Ressler, Kerry J; Almli, Lynn M; Zhang, Peng; Ma, Sean T; Cohen, Gregory H; Tamburrino, Marijo B; Calabrese, Joseph R; Galea, Sandro

    2014-10-01

    Posttraumatic stress disorder (PTSD), while highly prevalent (7.6% over a lifetime), develops only in a subset of trauma-exposed individuals. Genetic risk factors in interaction with trauma exposure have been implicated in PTSD vulnerability. To examine the association of 3755 candidate gene single-nucleotide polymorphisms with PTSD development in interaction with a history of childhood trauma. Genetic association study in an Ohio National Guard longitudinal cohort (n = 810) of predominantly male soldiers of European ancestry, with replication in an independent Grady Trauma Project (Atlanta, Georgia) cohort (n = 2083) of predominantly female African American civilians. Continuous measures of PTSD severity, with a modified (interview) PTSD checklist in the discovery cohort and the PTSD Symptom Scale in the replication cohort. Controlling for the level of lifetime adult trauma exposure, we identified the novel association of a single-nucleotide polymorphism within the promoter region of the ADRB2 (Online Mendelian Inheritance in Man 109690) gene with PTSD symptoms in interaction with childhood trauma (rs2400707, P = 1.02 × 10-5, significant after correction for multiple comparisons). The rs2400707 A allele was associated with relative resilience to childhood adversity. An rs2400707 × childhood trauma interaction predicting adult PTSD symptoms was replicated in the independent predominantly female African American cohort. Altered adrenergic and noradrenergic function has been long believed to have a key etiologic role in PTSD development; however, direct evidence of this link has been missing. The rs2400707 polymorphism has been linked to function of the adrenergic system, but, to our knowledge, this is the first study to date linking the ADRB2 gene to PTSD or any psychiatric disorders. These findings have important implications for PTSD etiology, chronic pain, and stress-related comorbidity, as well as for both primary prevention and treatment

  18. Blood pressure interacts with APOE ε4 to predict memory performance in a midlife sample.

    Science.gov (United States)

    Oberlin, Lauren E; Manuck, Stephen B; Gianaros, Peter J; Ferrell, Robert E; Muldoon, Matthew F; Jennings, J Richard; Flory, Janine D; Erickson, Kirk I

    2015-09-01

    Elevated blood pressure and the Apolipoprotein ε4 allele (APOE ε4) are independent risk factors for Alzheimer's disease. We sought to determine whether the combined presence of the APOE ε4 allele and elevated blood pressure is associated with lower cognitive performance in cognitively healthy middle-aged adults. A total of 975 participants aged 30-54 (mean age = 44.47) were genotyped for APOE. Cardiometabolic risk factors including blood pressure, lipids, and glucose were assessed and cognitive function was measured using the Trail Making Test and the Visual Reproduction and Logical Memory subtests from the Wechsler Memory Scale. Multivariable regression analysis showed that the association between APOE ε4 and episodic memory performance varied as a function of systolic blood pressure (SBP), such that elevated SBP was predictive of poorer episodic memory performance only in APOE ε4 carriers (β = -.092; t = -2.614; p = .009). Notably, this association was apparent at prehypertensive levels (≥130 mmHg), even after adjusting for physical activity, depression, smoking, and other cardiometabolic risk factors. The joint presence of APOE ε4 and elevated SBP, even at prehypertensive levels, is associated with lower cognitive performance in healthy, middle-aged adults. Results of this study suggest that the combination of APOE ε4 and elevated SBP may synergistically compromise memory function well before the appearance of clinically significant impairments. Interventions targeting blood pressure control in APOE ε4 carriers during midlife should be studied as a possible means to reduce the risk of cognitive decline in genetically susceptible samples. (c) 2015 APA, all rights reserved).

  19. Impulsivity interacts with momentary PTSD symptom worsening to predict alcohol use in male veterans.

    Science.gov (United States)

    Black, Anne C; Cooney, Ned L; Sartor, Carolyn E; Arias, Albert J; Rosen, Marc I

    2018-04-11

    Posttraumatic stress disorder (PTSD) is prevalent among veterans who served post-9/11, and co-occurs with problem alcohol and substance use. Studies using ecological momentary assessment have examined the temporal association between time-varying PTSD symptoms and alcohol use. Results suggest individual differences in these associations. We tested hypotheses that alcohol use measured by momentary assessment would be explained by acute increases in PTSD symptoms, and the PTSD-alcohol association would be moderated by trait impulsivity. A sample of 28 male post-9/11-era veterans who reported past-month PTSD symptoms and risky alcohol use were enrolled. On a quasi-random schedule, participants completed three electronic assessments daily for 28 days measuring past 2-h PTSD symptoms, alcohol, and substance use. At baseline, trait impulsivity was measured by the Barratt Impulsiveness Scale. Past-month PTSD symptoms and alcohol use were measured. Using three-level hierarchical models, number of drinks recorded by momentary assessment was modeled as a function of change in PTSD symptoms since last assessment, controlling for lag-1 alcohol and substance use and other covariates. A cross-level interaction tested moderation of the within-time PTSD-alcohol association by impulsivity. A total of 1,522 assessments were completed. A positive within-time association between PTSD symptom change and number of drinks was demonstrated. The association was significantly moderated by impulsivity. Results provide preliminary support for a unique temporal relationship between acute PTSD symptom change and alcohol use among veterans with trait impulsiveness. If replicated in a clinical sample, results may have implications for a targeted momentary intervention.

  20. A method for predicting errors when interacting with finite state systems. How implicit learning shapes the user's knowledge of a system

    International Nuclear Information System (INIS)

    Javaux, Denis

    2002-01-01

    This paper describes a method for predicting the errors that may appear when human operators or users interact with systems behaving as finite state systems. The method is a generalization of a method used for predicting errors when interacting with autopilot modes on modern, highly computerized airliners [Proc 17th Digital Avionics Sys Conf (DASC) (1998); Proc 10th Int Symp Aviat Psychol (1999)]. A cognitive model based on spreading activation networks is used for predicting the user's model of the system and its impact on the production of errors. The model strongly posits the importance of implicit learning in user-system interaction and its possible detrimental influence on users' knowledge of the system. An experiment conducted with Airbus Industrie and a major European airline on pilots' knowledge of autopilot behavior on the A340-200/300 confirms the model predictions, and in particular the impact of the frequencies with which specific state transitions and contexts are experienced

  1. Estimation of the physiological mechanical conditioning in vascular tissue engineering by a predictive fluid-structure interaction approach.

    Science.gov (United States)

    Tresoldi, Claudia; Bianchi, Elena; Pellegata, Alessandro Filippo; Dubini, Gabriele; Mantero, Sara

    2017-08-01

    The in vitro replication of physiological mechanical conditioning through bioreactors plays a crucial role in the development of functional Small-Caliber Tissue-Engineered Blood Vessels. An in silico scaffold-specific model under pulsatile perfusion provided by a bioreactor was implemented using a fluid-structure interaction (FSI) approach for viscoelastic tubular scaffolds (e.g. decellularized swine arteries, DSA). Results of working pressures, circumferential deformations, and wall shear stress on DSA fell within the desired physiological range and indicated the ability of this model to correctly predict the mechanical conditioning acting on the cells-scaffold system. Consequently, the FSI model allowed us to a priori define the stimulation pattern, driving in vitro physiological maturation of scaffolds, especially with viscoelastic properties.

  2. An efficient heuristic method for active feature acquisition and its application to protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Thahir Mohamed

    2012-11-01

    Full Text Available Abstract Background Machine learning approaches for classification learn the pattern of the feature space of different classes, or learn a boundary that separates the feature space into different classes. The features of the data instances are usually available, and it is only the class-labels of the instances that are unavailable. For example, to classify text documents into different topic categories, the words in the documents are features and they are readily available, whereas the topic is what is predicted. However, in some domains obtaining features may be resource-intensive because of which not all features may be available. An example is that of protein-protein interaction prediction, where not only are the labels ('interacting' or 'non-interacting' unavailable, but so are some of the features. It may be possible to obtain at least some of the missing features by carrying out a few experiments as permitted by the available resources. If only a few experiments can be carried out to acquire missing features, which proteins should be studied and which features of those proteins should be determined? From the perspective of machine learning for PPI prediction, it would be desirable that those features be acquired which when used in training the classifier, the accuracy of the classifier is improved the most. That is, the utility of the feature-acquisition is measured in terms of how much acquired features contribute to improving the accuracy of the classifier. Active feature acquisition (AFA is a strategy to preselect such instance-feature combinations (i.e. protein and experiment combinations for maximum utility. The goal of AFA is the creation of optimal training set that would result in the best classifier, and not in determining the best classification model itself. Results We present a heuristic method for active feature acquisition to calculate the utility of acquiring a missing feature. This heuristic takes into account the change in

  3. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences

    Directory of Open Access Journals (Sweden)

    Ji-Yong An

    2016-05-01

    Full Text Available Protein-Protein Interactions (PPIs play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM model and Average Blocks (AB to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed

  4. On topological RNA interaction structures.

    Science.gov (United States)

    Qin, Jing; Reidys, Christian M

    2013-07-01

    Recently a folding algorithm of topological RNA pseudoknot structures was presented in Reidys et al. (2011). This algorithm folds single-stranded γ-structures, that is, RNA structures composed by distinct motifs of bounded topological genus. In this article, we set the theoretical foundations for the folding of the two backbone analogues of γ structures: the RNA γ-interaction structures. These are RNA-RNA interaction structures that are constructed by a finite number of building blocks over two backbones having genus at most γ. Combinatorial properties of γ-interaction structures are of practical interest since they have direct implications for the folding of topological interaction structures. We compute the generating function of γ-interaction structures and show that it is algebraic, which implies that the numbers of interaction structures can be computed recursively. We obtain simple asymptotic formulas for 0- and 1-interaction structures. The simplest class of interaction structures are the 0-interaction structures, which represent the two backbone analogues of secondary structures.

  5. Inhibitory Control and Hedonic Response towards Food Interactively Predict Success in a Weight Loss Programme for Adults with Obesity

    Directory of Open Access Journals (Sweden)

    Timo Brockmeyer

    2016-10-01

    Full Text Available Objective: Low inhibitory control and strong hedonic response towards food are considered to contribute to overeating and obesity. Based on previous research, the present study aimed at examining the potentially crucial interplay between these two factors in terms of long-term weight loss in people with obesity. Methods: BMI, inhibitory control towards food, and food liking were assessed in obese adults prior to a weight reduction programme (OPTIFAST® 52. After the weight reduction phase (week 13 and the weight loss maintenance phase (week 52, participants' BMI was re-assessed. Results: Baseline BMI, inhibitory control and food liking alone did not predict weight loss. As hypothesised, however, inhibitory control and food liking interactively predicted weight loss from baseline to week 13 and to week 52 (albeit the latter effect was less robust. Participants with low inhibitory control and marked food liking were less successful in weight reduction. Conclusion: These findings underscore the relevance of the interplay between cognitive control and food reward valuation in the maintenance of obesity.

  6. The collective benefits of feeling good and letting go: positive emotion and (dis)inhibition interact to predict cooperative behavior.

    Science.gov (United States)

    Rand, David G; Kraft-Todd, Gordon; Gruber, June

    2015-01-01

    Cooperation is central to human existence, forming the bedrock of everyday social relationships and larger societal structures. Thus, understanding the psychological underpinnings of cooperation is of both scientific and practical importance. Recent work using a dual-process framework suggests that intuitive processing can promote cooperation while deliberative processing can undermine it. Here we add to this line of research by more specifically identifying deliberative and intuitive processes that affect cooperation. To do so, we applied automated text analysis using the Linguistic Inquiry and Word Count (LIWC) software to investigate the association between behavior in one-shot anonymous economic cooperation games and the presence inhibition (a deliberative process) and positive emotion (an intuitive process) in free-response narratives written after (Study 1, N = 4,218) or during (Study 2, N = 236) the decision-making process. Consistent with previous results, across both studies inhibition predicted reduced cooperation while positive emotion predicted increased cooperation (even when controlling for negative emotion). Importantly, there was a significant interaction between positive emotion and inhibition, such that the most cooperative individuals had high positive emotion and low inhibition. This suggests that inhibition (i.e., reflective or deliberative processing) may undermine cooperative behavior by suppressing the prosocial effects of positive emotion.

  7. The collective benefits of feeling good and letting go: positive emotion and (disinhibition interact to predict cooperative behavior.

    Directory of Open Access Journals (Sweden)

    David G Rand

    Full Text Available Cooperation is central to human existence, forming the bedrock of everyday social relationships and larger societal structures. Thus, understanding the psychological underpinnings of cooperation is of both scientific and practical importance. Recent work using a dual-process framework suggests that intuitive processing can promote cooperation while deliberative processing can undermine it. Here we add to this line of research by more specifically identifying deliberative and intuitive processes that affect cooperation. To do so, we applied automated text analysis using the Linguistic Inquiry and Word Count (LIWC software to investigate the association between behavior in one-shot anonymous economic cooperation games and the presence inhibition (a deliberative process and positive emotion (an intuitive process in free-response narratives written after (Study 1, N = 4,218 or during (Study 2, N = 236 the decision-making process. Consistent with previous results, across both studies inhibition predicted reduced cooperation while positive emotion predicted increased cooperation (even when controlling for negative emotion. Importantly, there was a significant interaction between positive emotion and inhibition, such that the most cooperative individuals had high positive emotion and low inhibition. This suggests that inhibition (i.e., reflective or deliberative processing may undermine cooperative behavior by suppressing the prosocial effects of positive emotion.

  8. The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

    Science.gov (United States)

    Okely, Judith A; Weiss, Alexander; Gale, Catharine R

    2018-02-01

    The link between greater wellbeing and longevity is well documented. The aim of the current study was to test whether this association is consistent across individualistic and collectivistic cultures. The sample consisted of 13,596 participants from 11 European countries, each of which was assigned an individualism score according to Hofstede et al.'s (Cultures and organizations: software of the mind, McGraw Hill, New York, 2010) cultural dimension of individualism. We tested whether individualism moderated the cross-sectional association between wellbeing and self-rated health or the longitudinal association between wellbeing and mortality risk. Our analysis revealed a significant interaction between individualism and wellbeing such that the association between wellbeing and self-rated health or risk of mortality from cardiovascular disease was stronger in more individualistic countries. However, the interaction between wellbeing and individualism was not significant in analysis predicting all-cause mortality. Further prospective studies are needed to confirm our finding and to explore the factors responsible for this culturally dependent effect.

  9. Dispositional pathways to trust: Self-esteem and agreeableness interact to predict trust and negative emotional disclosure.

    Science.gov (United States)

    McCarthy, Megan H; Wood, Joanne V; Holmes, John G

    2017-07-01

    Expressing our innermost thoughts and feelings is critical to the development of intimacy (Reis & Shaver, 1988), but also risks negative evaluation and rejection. Past research suggests that people with high self-esteem are more expressive and self-disclosing because they trust that others care for them and will not reject them (Gaucher et al., 2012). However, feeling good about oneself may not always be enough; disclosure may also depend on how we feel about other people. Drawing on the principles of risk regulation theory (Murray et al., 2006), we propose that agreeableness-a trait that refers to the positivity of interpersonal motivations and behaviors-is a key determinant of trust in a partner's caring and responsiveness, and may work in conjunction with self-esteem to predict disclosure. We examined this possibility by exploring how both self-esteem and agreeableness predict a particularly risky and intimate form of self-disclosure, the disclosure of emotional distress. In 6 studies using correlational, partner-report, and experimental methods, we demonstrate that self-esteem and agreeableness interact to predict disclosure: People who are high in both self-esteem and agreeableness show higher emotional disclosure. We also found evidence that trust mediates this effect. People high in self-esteem and agreeableness are most self-revealing, it seems, because they are especially trusting of their partners' caring. Self-esteem and agreeableness were particularly important for the disclosure of vulnerable emotions (i.e., sadness; Study 5) and disclosures that were especially risky (Study 6). These findings illustrate how dispositional variables can work together to explain behavior in close relationships. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  10. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

    Science.gov (United States)

    Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

    2015-03-01

    Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new

  11. Systems Biology Analysis of Temporal In vivo Brucella melitensis and Bovine Transcriptomes Predicts host:Pathogen Protein–Protein Interactions

    Directory of Open Access Journals (Sweden)

    Carlos A. Rossetti

    2017-07-01

    Full Text Available To date, fewer than 200 gene-products have been identified as Brucella virulence factors, and most were characterized individually without considering how they are temporally and coordinately expressed or secreted during the infection process. Here, we describe and analyze the in vivo temporal transcriptional profile of Brucella melitensis during the initial 4 h interaction with cattle. Pathway analysis revealed an activation of the “Two component system” providing evidence that the in vivo Brucella sense and actively regulate their metabolism through the transition to an intracellular lifestyle. Contrarily, other Brucella pathways involved in virulence such as “ABC transporters” and “T4SS system” were repressed suggesting a silencing strategy to avoid stimulation of the host innate immune response very early in the infection process. Also, three flagellum-encoded loci (BMEII0150-0168, BMEII1080-1089, and BMEII1105-1114, the “flagellar assembly” pathway and the cell components “bacterial-type flagellum hook” and “bacterial-type flagellum” were repressed in the tissue-associated B. melitensis, while RopE1 sigma factor, a flagellar repressor, was activated throughout the experiment. These results support the idea that Brucella employ a stealthy strategy at the onset of the infection of susceptible hosts. Further, through systems-level in silico host:pathogen protein–protein interactions simulation and correlation of pathogen gene expression with the host gene perturbations, we identified unanticipated interactions such as VirB11::MAPK8IP1; BtaE::NFKBIA, and 22 kDa OMP precursor::BAD and MAP2K3. These findings are suggestive of new virulence factors and mechanisms responsible for Brucella evasion of the host's protective immune response and the capability to maintain a dormant state. The predicted protein–protein interactions and the points of disruption provide novel insights that will stimulate advanced hypothesis

  12. Effects of air-sea interaction on extended-range prediction of geopotential height at 500 hPa over the northern extratropical region

    Science.gov (United States)

    Wang, Xujia; Zheng, Zhihai; Feng, Guolin

    2018-04-01

    The contribution of air-sea interaction on the extended-range prediction of geopotential height at 500 hPa in the northern extratropical region has been analyzed with a coupled model form Beijing Climate Center and its atmospheric components. Under the assumption of the perfect model, the extended-range prediction skill was evaluated by anomaly correlation coefficient (ACC), root mean square error (RMSE), and signal-to-noise ratio (SNR). The coupled model has a better prediction skill than its atmospheric model, especially, the air-sea interaction in July made a greater contribution for the improvement of prediction skill than other months. The prediction skill of the extratropical region in the coupled model reaches 16-18 days in all months, while the atmospheric model reaches 10-11 days in January, April, and July and only 7-8 days in October, indicating that the air-sea interaction can extend the prediction skill of the atmospheric model by about 1 week. The errors of both the coupled model and the atmospheric model reach saturation in about 20 days, suggesting that the predictable range is less than 3 weeks.

  13. Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

    Directory of Open Access Journals (Sweden)

    Sivakumar Sukumaran

    2017-02-01

    Full Text Available Developing genomic selection (GS models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY, thousand-grain weight (GW, grain number (GN, and thermal time for flowering (TTF in 18 international environments (year-location combinations in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1, and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2. Genomic prediction models, including genotype × environment (G×E interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31, GN (0.32, GW (0.45, and TTF (0.27. For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38, GN (0.43, GW (0.63, and TTF (0.53. Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting G×E interaction in multi-environment trials.

  14. A strategy for early-risk predictions of clinical drug-drug interactions involving the GastroPlusTM DDI module for time-dependent CYP inhibitors.

    Science.gov (United States)

    Sohlenius-Sternbeck, Anna-Karin; Meyerson, Gabrielle; Hagbjörk, Ann-Louise; Juric, Sanja; Terelius, Ylva

    2018-04-01

    1. A set of reference compounds for time-dependent inhibition (TDI) of cytochrome P450 with available literature data for k inact and K I was used to predict clinical implications using the GastroPlus TM software. Comparisons were made to in vivo literature interaction data. 2. The predicted AUC ratios (AUC +inhibitor /AUC control ) could be compared with the observed ratios from literature for all compounds with detailed information about in vivo administration, pharmacokinetics and in vivo interactions (N = 21). For this dataset, the difference between predicted and observed AUC ratios for interactions with midazolam was within twofold for all compounds except one (telaprevir, for which non-CYP-mediated metabolism likely plays a role after multiple dosing). 3. The sensitivity, specificity and accuracy of the GastroPlus TM predictions using a binary classification as no-to-weak interaction versus moderate-to-strong interaction for all compounds with available in vivo interaction data, were 80%, 82% and 81%, respectively (N = 31). 4. As a result of our evaluations of the DDI module in GastroPlus TM , we have implemented an early TDI risk assessment decision tree for our drug discovery projects involving in vitro screening and early GastroPlus TM predictions. Shifted IC 50 values are determined and k inact /K I estimated (by using a regression line established with in house-shifted IC 50 values and literature k inact /K I ratios), followed by GastroPlus TM predictions.

  15. Acid-Gangue Interactions in Heap Leach Operations: A Review of the Role of Mineralogy for Predicting Ore Behaviour

    Directory of Open Access Journals (Sweden)

    Deshenthree Chetty

    2018-01-01

    Full Text Available Heap leaching accounts for a fifth of global copper production, sourced primarily from porphyry ores, yet metal recoveries are often not optimal. Gangue, and its interaction with acid, plays an important role in such processes. Thus, a proper understanding of gangue minerals present in the ore, their textural relationships relative to particle size distribution, reactivity with acid under different conditions, and relationship to lithotypes and geological alteration in the orebody, is necessary to predict ore behaviour in the comminution, agglomeration, curing and heap leach unit operations. Mineralogical tools available for characterisation are routine X-ray diffraction, optical microscopy, automated scanning electron microscopy, and electron probe microanalysis, accompanied by more recent advancements in hyperspectral infrared imaging and X-ray computed tomography. Integrated use of these techniques allows mineral abundance, textural relationships and mineral chemistry to be addressed over the range of particle and agglomerate sizes. Additionally, diagnostic leach results can be better interpreted when calibrated against robust mineralogical data. The linkage of ore attributes, metallurgical behaviour and their distribution in the orebody forms an integral part of a geometallurgical approach to predicting, and addressing, changes during the heap leaching process. Further investigation should address the fundamentals of gangue reaction with strong acid, and concomitant structural breakdown during curing and agglomeration processes, and how this differs from gangue-acid reactivity under weaker acid conditions, combined with temperature and fluid flow effects of heap leaching. Pre-and post- characterisation is necessary to understand and quantify the effects of variables for gangue-acid reactivity in these various operations. The characterisation outcomes should lead to a refinement of the hierarchy of gangue mineral reactivity under different

  16. A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

    Science.gov (United States)

    Cherkaoui-Rbati, Mohammed H; Paine, Stuart W; Littlewood, Peter; Rauch, Cyril

    2017-01-01

    All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

  17. A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

    Directory of Open Access Journals (Sweden)

    Mohammed H Cherkaoui-Rbati

    Full Text Available All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs of new chemical entities (NCEs and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit, located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level. A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

  18. The cytochrome P450 isoenzyme and some new opportunities for the prediction of negative drug interaction in vivo

    Directory of Open Access Journals (Sweden)

    Sychev DA

    2018-05-01

    Full Text Available Dmitrij A Sychev,1 Ghulam Md Ashraf,2 Andrey A Svistunov,3 Maksim L Maksimov,4 Vadim V Tarasov,3 Vladimir N Chubarev,3 Vitalij A Otdelenov,1 Natal’ja P Denisenko,1 George E Barreto,5,6 Gjumrakch Aliev7–9 1Russian Medical Academy of Postgraduate Education Studies, Moscow, Russia; 2King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia; 3Sechenov First Moscow State Medical University, Moscow, Russia; 4Branch Campus of the Federal State Budgetary Educational Institution of Further Professional Education «Russian Medical Academy of Continuous Professional Education» of the Ministry of Healthcare of the Russian Federation, Kazan State Medical Academy, Volga Region, Kazan, Russia; 5Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; 6Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Santiago, Chile; 7GALLY International Biomedical Research Consulting LLC, San Antonio, TX, USA; 8School of Health Science and Healthcare Administration, University of Atlanta, Johns Creek, GA, USA; 9Institute of Physiologically Active Compounds Russian Academy of Sciences, Chernogolovka, Russia Abstract: Cytochrome (CYP 450 isoenzymes are the basic enzymes involved in Phase I biotransformation. The most important role in biotransformation belongs to CYP3A4, CYP2D6, CYP2C9, CYP2C19 and CYP1A2. Inhibition and induction of CYP isoenzymes caused by drugs are important and clinically relevant pharmacokinetic mechanisms of drug interaction. Investigation of the activity of CYP isoenzymes by using phenotyping methods (such as the determination of the concentration of specific substrates and metabolites in biological fluids during drug administration provides the prediction of negative side effects caused by drug interaction. In clinical practice, the process of phenotyping of CYP isoenzymes and some endogenous substrates in the ratio of cortisol to 6

  19. An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

    Science.gov (United States)

    Liu, Tao; Chen, Lei; Pan, Xiaoyong

    2018-05-31

    Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Interaction between physiological and subjective states predicts the effect of a judging panel on the postures of cellists in performance

    Directory of Open Access Journals (Sweden)

    Satoshi eEndo

    2014-08-01

    Full Text Available This study investigated the effect of a panel of judges on the movements and postures of cellists in performance. 24 expert cellists played a short piece of music, to a metronome beat, in the presence and absence of the panel. Kinematic analyses showed that in the presence of the panel the temporal execution of left arm shifting movements became less variable and closer to the metronome beat. In contrast, the panel's presence had no reliable effect on their spatial accuracy. A detailed postural analysis indicated that left elbow angle during execution of a given high note was correlated with level of heart rate, though the nature of this correlation was systematically affected by the relevant participant's subjective state: if anxious, a higher heart rate correlated with a more flexed elbow, if not anxious then with a more extended elbow. Our results suggest a change in physiological state alone does not reliably predict a change in behaviour in performing cellists, which instead depends on the interaction between physiological state and subjective experience of anxiety. This highlights a need to distinguish performance anxiety from physiological arousal, to which end we advocate currency for the specific term performance arousal to describe heightened physiological activity in a performer.

  1. Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server.

    KAUST Repository

    Olimpieri, Pier Paolo

    2013-06-26

    MOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

  2. High energy density physics effects predicted in simulations of the CERN HiRadMat beam-target interaction experiments

    Science.gov (United States)

    Tahir, N. A.; Burkart, F.; Schmidt, R.; Shutov, A.; Wollmann, D.; Piriz, A. R.

    2016-12-01

    Experiments have been done at the CERN HiRadMat (High Radiation to Materials) facility in which large cylindrical copper targets were irradiated with 440 GeV proton beam generated by the Super Proton Synchrotron (SPS). The primary purpose of these experiments was to confirm the existence of hydrodynamic tunneling of ultra-relativistic protons and their hadronic shower in solid materials, that was predicted by previous numerical simulations. The experimental measurements have shown very good agreement with the simulation results. This provides confidence in our simulations of the interaction of the 7 TeV LHC (Large Hadron Collider) protons and the 50 TeV Future Circular Collider (FCC) protons with solid materials, respectively. This work is important from the machine protection point of view. The numerical simulations have also shown that in the HiRadMat experiments, a significant part of thetarget material is be converted into different phases of High Energy Density (HED) matter, including two-phase solid-liquid mixture, expanded as well as compressed hot liquid phases, two-phase liquid-gas mixture and gaseous state. The HiRadMat facility is therefore a unique ion beam facility worldwide that is currently available for studying the thermophysical properties of HED matter. In the present paper we discuss the numerical simulation results and present a comparison with the experimental measurements.

  3. The Dark Side of Authenticity: Feeling "Real" While Gambling Interacts with Enhancement Motives to Predict Problematic Gambling Behavior.

    Science.gov (United States)

    Lister, Jamey J; Wohl, Michael J A; Davis, Christopher G

    2015-09-01

    Engaging in activities that make people feel authentic or real is typically associated with a host of positive psychological and physiological outcomes (i.e., being authentic serves to increase well-being). In the current study, we tested the idea that authenticity might have a dark side among people engaged in an addictive or risky behavior (gambling). To test this possibility, we assessed gamblers (N = 61) who were betting on the National Hockey League playoff games at a sports bar. As predicted, people who felt authentic when gambling reported behavior associated with problem gambling (high frequency of betting) as well as problematic play (a big monetary loss and a big monetary win). Moreover, such behavior and gambling outcomes were particularly high among people who were motivated to gamble for the purpose of enhancement. The interaction of feeling authentic when betting and gambling for purposes of enhancing positive emotions proved especially troublesome for problematic forms of play. Implications of authenticity as a potential vulnerability factor for sports betting and other types of gambling are discussed.

  4. Predicting Heavy Alcohol Use in College Students: Interactions Among Socialization of Coping, Alcohol Use Onset, and Physiological Reactivity.

    Science.gov (United States)

    Stanger, Sarah; Abaied, Jamie; Wagner, Caitlin

    2016-05-01

    Early age at onset of alcohol use is a risk factor for later heavy alcohol use, but some individuals are buffered from this risk. To better understand this process, this study investigated the interactive contributions of parental coping suggestions, skin conductance level reactivity (SCLR), and age at onset of alcohol use on heavy alcohol use in college students. College students (N = 146, 77% female) reported their age at onset of alcohol use, frequency of recent heavy alcohol use, and their parents' coping suggestions; SCLR was monitored as participants completed a laboratory challenge task. In addition, students' parents (N = 73, 77% mothers) reported on their coping suggestions. Results indicated that in the presence of physiological risk only (blunted SCLR, late age at onset of alcohol use), higher frequencies of engagement and disengagement parental coping suggestions were protective against heavy alcohol use in college students. However, if both risk factors were present (blunted SCLR, early age at onset of alcohol use), more engagement suggestions predicted more heavy alcohol use among college students. These findings extend previous findings on the impact of parenting on heavy alcohol use among college students and provide novel evidence for the moderating role of sympathetic stress reactivity.

  5. Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents

    NARCIS (Netherlands)

    Thush, C.; Wiers, R.W.H.J.; Ames, S.L.; Grenard, J.L.; Sussman, S.Y.; Stacy, A.W.

    2008-01-01

    Dual process models of addiction suggest that the influence of alcohol-related cognition might be dependent on the level of executive functioning. This study investigated if the interaction between implicit and explicit alcohol-related cognitions and working memory capacity predicted alcohol use

  6. A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

    Science.gov (United States)

    Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

    2014-01-21

    Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  7. Mixed culture models for predicting intestinal microbial interactions between Escherichia coli and Lactobacillus in the presence of probiotic Bacillus subtilis.

    Science.gov (United States)

    Yang, J J; Niu, C C; Guo, X H

    2015-01-01

    Bacillus has been proposed as a probiotic due to its in vivo effectiveness in the gastrointestinal tract through antimicrobial activities. The present study investigates the effects of Lactobacillus alone or in the presence of Bacillus subtilis MA139 on the inhibition of pathogenic Escherichia coli K88. Mixed cultures were used to predict the possible interactions among these bacteria within the intestinal tract of animals. B. subtilis MA139 was first assayed for its inhibition against E. coli K88 both under shaking and static culture conditions. A co-culture assay was employed under static conditions to test the inhibitory effects of Lactobacillus reuteri on E. coli K88, with or without addition of B. subtilis MA139. The results showed that B. subtilis MA139 had marked inhibition against E. coli K88 under shaking conditions and weak inhibition under static conditions. Lactobacillus alone as well as in combination with B. subtilis MA139 spores exerted strong inhibition against E. coli K88 under static conditions. However, the inhibition by Lactobacillus in combination with B. subilis spores was much higher than that by Lactobacillus alone (Psubtilis MA139 significantly decreased the pH and oxidation-reduction potential values of the co-culture broth compared to that of Lactobacillus alone (Psubtilis MA139 because of significantly higher Lactobacillus counts and lower pH values in the broth (PBacillus in the mixed culture models suggests that Bacillus may produce beneficial effects by increasing the viability of lactobacilli and subsequently inhibiting the growth of pathogenic E. coli. Therefore, the combination of Bacillus and Lactobacillus species as a probiotic is recommended.

  8. Non-linear finite element analysis for prediction of seismic response of buildings considering soil-structure interaction

    Directory of Open Access Journals (Sweden)

    E. Çelebi

    2012-11-01

    Full Text Available The objective of this paper focuses primarily on the numerical approach based on two-dimensional (2-D finite element method for analysis of the seismic response of infinite soil-structure interaction (SSI system. This study is performed by a series of different scenarios that involved comprehensive parametric analyses including the effects of realistic material properties of the underlying soil on the structural response quantities. Viscous artificial boundaries, simulating the process of wave transmission along the truncated interface of the semi-infinite space, are adopted in the non-linear finite element formulation in the time domain along with Newmark's integration. The slenderness ratio of the superstructure and the local soil conditions as well as the characteristics of input excitations are important parameters for the numerical simulation in this research. The mechanical behavior of the underlying soil medium considered in this prediction model is simulated by an undrained elasto-plastic Mohr-Coulomb model under plane-strain conditions. To emphasize the important findings of this type of problems to civil engineers, systematic calculations with different controlling parameters are accomplished to evaluate directly the structural response of the vibrating soil-structure system. When the underlying soil becomes stiffer, the frequency content of the seismic motion has a major role in altering the seismic response. The sudden increase of the dynamic response is more pronounced for resonance case, when the frequency content of the seismic ground motion is close to that of the SSI system. The SSI effects under different seismic inputs are different for all considered soil conditions and structural types.

  9. Comparative Human and Automatic Evaluation of Glass-Box and Black-Box Approaches to Interactive Translation Prediction

    Directory of Open Access Journals (Sweden)

    Torregrosa Daniel

    2017-06-01

    Full Text Available Interactive translation prediction (ITP is a modality of computer-aided translation that assists professional translators by offering context-based computer-generated continuation suggestions as they type. While most state-of-the-art ITP systems follow a glass-box approach, meaning that they are tightly coupled to an adapted machine translation system, a black-box approach which does not need access to the inner workings of the bilingual resources used to generate the suggestions has been recently proposed in the literature: this new approach allows new sources of bilingual information to be included almost seamlessly. In this paper, we compare for the first time the glass-box and the black-box approaches by means of an automatic evaluation of translation tasks between related languages such as English–Spanish and unrelated ones such as Arabic–English and English–Chinese, showing that, with our setup, 20%–50% of keystrokes could be saved using either method and that the black-box approach outperformed the glass-box one in five out of six scenarios operating under similar conditions. We also performed a preliminary human evaluation of English to Spanish translation for both approaches. On average, the evaluators saved 10% keystrokes and were 4% faster with the black-box approach, and saved 15% keystrokes and were 12% slower with the glass-box one; but they could have saved 51% and 69% keystrokes respectively if they had used all the compatible suggestions. Users felt the suggestions helped them to translate faster and easier. All the tools used to perform the evaluation are available as free/open–source software.

  10. Computational prediction of secretion systems and secretomes of Brucella: identification of novel type IV effectors and their interaction with the host.

    Science.gov (United States)

    Sankarasubramanian, Jagadesan; Vishnu, Udayakumar S; Dinakaran, Vasudevan; Sridhar, Jayavel; Gunasekaran, Paramasamy; Rajendhran, Jeyaprakash

    2016-01-01

    Brucella spp. are facultative intracellular pathogens that cause brucellosis in various mammals including humans. Brucella survive inside the host cells by forming vacuoles and subverting host defence systems. This study was aimed to predict the secretion systems and the secretomes of Brucella spp. from 39 complete genome sequences available in the databases. Furthermore, an attempt was made to identify the type IV secretion effectors and their interactions with host proteins. We predicted the secretion systems of Brucella by the KEGG pathway and SecReT4. Brucella secretomes and type IV effectors (T4SEs) were predicted through genome-wide screening using JVirGel and S4TE, respectively. Protein-protein interactions of Brucella T4SEs with their hosts were analyzed by HPIDB 2.0. Genes coding for Sec and Tat pathways of secretion and type I (T1SS), type IV (T4SS) and type V (T5SS) secretion systems were identified and they are conserved in all the species of Brucella. In addition to the well-known VirB operon coding for the type IV secretion system (T4SS), we have identified the presence of additional genes showing homology with T4SS of other organisms. On the whole, 10.26 to 14.94% of total proteomes were found to be either secreted (secretome) or membrane associated (membrane proteome). Approximately, 1.7 to 3.0% of total proteomes were identified as type IV secretion effectors (T4SEs). Prediction of protein-protein interactions showed 29 and 36 host-pathogen specific interactions between Bos taurus (cattle)-B. abortus and Ovis aries (sheep)-B. melitensis, respectively. Functional characterization of the predicted T4SEs and their interactions with their respective hosts may reveal the secrets of host specificity of Brucella.

  11. The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique

    KAUST Repository

    Theofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A.; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P.

    2013-01-01

    Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about

  12. In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

    Science.gov (United States)

    Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

    2017-07-01

    Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

    Directory of Open Access Journals (Sweden)

    Luan Yihui

    2009-09-01

    Full Text Available Abstract Background Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Results Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Conclusion Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

  14. Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach.

    Science.gov (United States)

    Wang, Wenhui; Nunez-Iglesias, Juan; Luan, Yihui; Sun, Fengzhu

    2009-09-03

    Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

  15. The dopamine receptor D4 gene and familial loading interact with perceived parenting in predicting externalizing behavior problems in early adolescence: the TRacking Adolescents' Individual Lives Survey (TRAILS).

    Science.gov (United States)

    Marsman, Rianne; Oldehinkel, Albertine J; Ormel, Johan; Buitelaar, Jan K

    2013-08-30

    Although externalizing behavior problems show in general a high stability over time, the course of externalizing behavior problems may vary from individual to individual. Our main goal was to investigate the predictive role of parenting on externalizing behavior problems. In addition, we investigated the potential moderating role of gender and genetic risk (operationalized as familial loading of externalizing behavior problems (FLE), and presence or absence of the dopamine receptor D4 (DRD4) 7-repeat and 4-repeat allele, respectively). Perceived parenting (rejection, emotional warmth, and overprotection) and FLE were assessed in a population-based sample of 1768 10- to 12-year-old adolescents. Externalizing behavior problems were assessed at the same age and 212 years later by parent report (CBCL) and self-report (YSR). DNA was extracted from blood samples. Parental emotional warmth predicted lower, and parental overprotection and rejection predicted higher levels of externalizing behavior problems. Whereas none of the parenting factors interacted with gender and the DRD4 7-repeat allele, we did find interaction effects with FLE and the DRD4 4-repeat allele. That is, the predictive effect of parental rejection was only observed in adolescents from low FLE families and the predictive effect of parental overprotection was stronger in adolescents not carrying the DRD4 4-repeat allele. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  16. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

    Directory of Open Access Journals (Sweden)

    Han Kyungsook

    2010-06-01

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

  17. 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…

  18. Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model

    Directory of Open Access Journals (Sweden)

    Irena Cosic

    2016-06-01

    Full Text Available The meaning and influence of light to biomolecular interactions, and consequently to health, has been analyzed using the Resonant Recognition Model (RRM. The RRM proposes that biological processes/interactions are based on electromagnetic resonances between interacting biomolecules at specific electromagnetic frequencies within the infra-red, visible and ultra-violet frequency ranges, where each interaction can be identified by the certain frequency critical for resonant activation of specific biological activities of proteins and DNA. We found that: (1 the various biological interactions could be grouped according to their resonant frequency into super families of these functions, enabling simpler analyses of these interactions and consequently analyses of influence of electromagnetic frequencies to health; (2 the RRM spectrum of all analyzed biological functions/interactions is the same as the spectrum of the sun light on the Earth, which is in accordance with fact that life is sustained by the sun light; (3 the water is transparent to RRM frequencies, enabling proteins and DNA to interact without loss of energy; (4 the spectrum of some artificial sources of light, as opposed to the sun light, do not cover the whole RRM spectrum, causing concerns for disturbance to some biological functions and consequently we speculate that it can influence health.

  19. Numerical Investigation on the Effects of Self-Excited Tip Flow Unsteadiness and Blade Row Interactions on the Performance Predictions of Low Speed and Transonic Compressor Rotors

    Science.gov (United States)

    Lee, Daniel H.

    The impact blade row interactions can have on the performance of compressor rotors has been well documented. It is also well known that rotor tip clearance flows can have a large effect on compressor performance and stall margin and recent research has shown that tip leakage flows can exhibit self-excited unsteadiness at near stall conditions. However, the impact of tip leakage flow on the performance and operating range of a compressor rotor, relative to other important flow features such as upstream stator wakes or downstream potential effects, has not been explored. To this end, a numerical investigation has been conducted to determine the effects of self-excited tip flow unsteadiness, upstream stator wakes, and downstream blade row interactions on the performance prediction of low speed and transonic compressor rotors. Calculations included a single blade-row rotor configuration as well as two multi-blade row configurations: one where the rotor was modeled with an upstream stator and a second where the rotor was modeled with a downstream stator. Steady-state and time accurate calculations were performed using a RANS solver and the results were compared with detailed experimental data obtained in the GE Low Speed Research Compressor and the Notre