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Sample records for rna-rna interaction prediction

  1. RNA-RNA interaction prediction based on multiple sequence alignments

    CERN Document Server

    Li, Andrew X; Qin, Jing; Reidys, Christian M

    2010-01-01

    Recently, $O(N^6)$ time and $O(N^4)$ space dynamic programming algorithms have become available that compute the partition function of RNA-RNA interaction complexes for pairs of RNA sequences. These algorithms and the biological requirement of more reliable interactions motivate to utilize the additional information contained in multiple sequence alignments and to generalize the above framework to the partition function and base pairing probabilities for multiple sequence alignments.

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

    Directory of Open Access Journals (Sweden)

    Gorodkin Jan

    2010-05-01

    Full Text Available Abstract Background Many regulatory non-coding RNAs (ncRNAs function through complementary binding with mRNAs or other ncRNAs, e.g., microRNAs, snoRNAs and bacterial sRNAs. Predicting these RNA interactions is essential for functional studies of putative ncRNAs or for the design of artificial RNAs. Many ncRNAs show clear signs of undergoing compensating base changes over evolutionary time. Here, we postulate that a non-negligible part of the existing RNA-RNA interactions contain preserved but covarying patterns of interactions. Methods We present a novel method that takes compensating base changes across the binding sites into account. The algorithm works in two steps on two pre-generated multiple alignments. In the first step, individual base pairs with high reliability are found using the PETfold algorithm, which includes evolutionary and thermodynamic properties. In step two (where high reliability base pairs from step one are constrained as unpaired, the principle of cofolding is combined with hierarchical folding. The final prediction of intra- and inter-molecular base pairs consists of the reliabilities computed from the constrained expected accuracy scoring, which is an extended version 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 RNA-RNA 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.

  3. RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets

    DEFF Research Database (Denmark)

    Alkan, Ferhat; Wenzel, Anne; Palasca, Oana

    2017-01-01

    and high level of complementarity between two RNA sequences is a powerful predictor of such interactions. Here, we present RIsearch2, a large-scale RNA-RNA interaction prediction tool that enables quick localization of potential near-complementary RNA-RNA interactions between given query and target...

  4. Target prediction and a statistical sampling algorithm for RNA-RNA interaction

    CERN Document Server

    Huang, F W D; Reidys, C M; Stadler, P F

    2009-01-01

    It has been proven that the accessibility of the target sites has a critical influence for miRNA and siRNA. In this paper, we present a program, rip2.0, not only the energetically most favorable targets site based on the hybrid-probability, but also a statistical sampling structure to illustrate the statistical characterization and representation of the Boltzmann ensemble of RNA-RNA interaction structures. The outputs are retrieved via backtracing an improved dynamic programming solution for the partition function based on the approach of Huang et al. (Bioinformatics). The $O(N^6)$ time and $O(N^4)$ space algorithm is implemented in C (available from \\url{http://www.combinatorics.cn/cbpc/rip2.html})

  5. 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...

  6. 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

    Background: Many regulatory non-coding RNAs (ncRNAs) function through complementary binding with mRNAs or other ncRNAs, e.g., microRNAs, snoRNAs and bacterial sRNAs. Predicting these RNA interactions is essential for functional studies of putative ncRNAs or for the design of artificial RNAs. Many...

  7. Combinatorics of RNA-RNA interaction.

    Science.gov (United States)

    Li, Thomas J X; Reidys, Christian M

    2012-02-01

    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 their generating function, singularity analysis as well as explicit recurrence relations. In particular, our results imply simple asymptotic formulas for the number of joint structures.

  8. Combinatorics of RNA-RNA interaction

    CERN Document Server

    Li, Thomas J X

    2010-01-01

    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 ``zig-zag'' configuration. This paper presents the combinatorics of RNA interaction structures including their generating function, singularity analysis as well as explicit recurrence relations. In particular, our results imply simple asymptotic formulas for the number of joint structures.

  9. RAID: a comprehensive resource for human RNA-associated (RNA-RNA/RNA-protein) interaction.

    Science.gov (United States)

    Zhang, Xiaomeng; Wu, Deng; Chen, Liqun; Li, Xiang; Yang, Jinxurong; Fan, Dandan; Dong, Tingting; Liu, Mingyue; Tan, Puwen; Xu, Jintian; Yi, Ying; Wang, Yuting; Zou, Hua; Hu, Yongfei; Fan, Kaili; Kang, Juanjuan; Huang, Yan; Miao, Zhengqiang; Bi, Miaoman; Jin, Nana; Li, Kongning; Li, Xia; Xu, Jianzhen; Wang, Dong

    2014-07-01

    Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA-RNA/RNA-protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA-RNA interactions and 1619 RNA-protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA-RNA/RNA-protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA-RNA/RNA-protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network.

  10. 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...

  11. Topology of RNA-RNA interaction structures

    CERN Document Server

    Andersen, Jørgen E; Penner, Robert C; Reidys, Christian M

    2011-01-01

    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 for fixed genus only finitely many irreducible shadows. This implies that for fixed genus there are only finitely many classes of interaction structures. In particular the simplest case of genus zero already provides the formalism for certain types of structures that occur in nature and are not covered by other filtrations. This case of genus zero interaction structures is already of practical interest, is studied here in detail and found to be expressed by a multiple context-free grammar extending the usual one for RNA secondary structures. We show that in $O(n^6)$ time and $O(n^4)$ space complexity, this grammar for genus zero interaction structures provides not only minimum free energy solutions but also the complete partit...

  12. 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.

  13. RNA:RNA interaction can enhance RNA localization in Drosophila oocytes.

    Science.gov (United States)

    Hartswood, Eve; Brodie, Jim; Vendra, Georgia; Davis, Ilan; Finnegan, David J

    2012-04-01

    RNA localization is a key mechanism for targeting proteins to particular subcellular domains. Sequences necessary and sufficient for localization have been identified, but little is known about factors that affect its kinetics. Transcripts of gurken and the I factor, a non-LTR retrotransposon, colocalize at the nucleus in the dorso-antero corner of the Drosophila oocyte directed by localization signals, the GLS and ILS. I factor RNA localizes faster than gurken after injection into oocytes, due to a difference in the intrinsic localization ability of the GLS and ILS. The kinetics of localization of RNA containing the ILS are enhanced by the presence of a stem-loop, the A loop. This acts as an RNA:RNA interaction element in vivo and in vitro, and stimulates localization of RNA containing other localization signals. RNA:RNA interaction may be a general mechanism for modulating RNA localization and could allow an mRNA that lacks a localization signal to hitchhike on another RNA that has one.

  14. 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

  15. Single-molecule observations of RNA-RNA kissing interactions in a DNA nanostructure.

    Science.gov (United States)

    Takeuchi, Yosuke; Endo, Masayuki; Suzuki, Yuki; Hidaka, Kumi; Durand, Guillaume; Dausse, Eric; Toulmé, Jean-Jacques; Sugiyama, Hiroshi

    2016-01-01

    RNA molecules uniquely form a complex through specific hairpin loops, called a kissing complex. The kissing complex is widely investigated and used for the construction of RNA nanostructures. Molecular switches have also been created by combining a kissing loop and a ligand-binding aptamer to control the interactions of RNA molecules. In this study, we incorporated two kinds of RNA molecules into a DNA origami structure and used atomic force microscopy to observe their ligand-responsive interactions at the single-molecule level. We used a designed RNA aptamer called GTPswitch, which has a guanosine triphosphate (GTP) responsive domain and can bind to the target RNA hairpin named Aptakiss in the presence of GTP. We observed shape changes of the DNA/RNA strands in the DNA origami, which are induced by the GTPswitch, into two different shapes in the absence and presence of GTP, respectively. We also found that the switching function in the nanospace could be improved by using a cover strand over the kissing loop of the GTPswitch or by deleting one base from this kissing loop. These newly designed ligand-responsive aptamers can be used for the controlled assembly of the various DNA and RNA nanostructures.

  16. Structure and stability of RNA/RNA kissing complex: with application to HIV dimerization initiation signal.

    Science.gov (United States)

    Cao, Song; Chen, Shi-Jie

    2011-12-01

    We develop a statistical mechanical model to predict the structure and folding stability of the RNA/RNA kissing-loop complex. One of the key ingredients of the theory is the conformational entropy for the RNA/RNA kissing complex. We employ the recently developed virtual bond-based RNA folding model (Vfold model) to evaluate the entropy parameters for the different types of kissing loops. A benchmark test against experiments suggests that the entropy calculation is reliable. As an application of the model, we apply the model to investigate the structure and folding thermodynamics for the kissing complex of the HIV-1 dimerization initiation signal. With the physics-based energetic parameters, we compute the free energy landscape for the HIV-1 dimer. From the energy landscape, we identify two minimal free energy structures, which correspond to the kissing-loop dimer and the extended-duplex dimer, respectively. The results support the two-step dimerization process for the HIV-1 replication cycle. Furthermore, based on the Vfold model and energy minimization, the theory can predict the native structure as well as the local minima in the free energy landscape. The root-mean-square deviations (RMSDs) for the predicted kissing-loop dimer and extended-duplex dimer are ~3.0 Å. The method developed here provides a new method to study the RNA/RNA kissing complex.

  17. 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

  18. 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

  19. Interactions of timing and prediction error learning.

    Science.gov (United States)

    Kirkpatrick, Kimberly

    2014-01-01

    Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields.

  20. 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.

  1. 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 $\

  2. Interactive Appearance Prediction for Cloudy Beverages

    DEFF Research Database (Denmark)

    Dal Corso, Alessandro; Frisvad, Jeppe Revall; Kjeldsen, Thomas Kim;

    2016-01-01

    Juice appearance is important to consumers, so digital juice with a slider that varies a production parameter or changes juice content is useful. It is however challenging to render juice with scattering particles quickly and accurately. As a case study, we create an appearance model that provides...... the optical properties needed for rendering of unfiltered apple juice. This is a scattering medium that requires volume path tracing as the scattering is too much for single scattering techniques and too little for subsurface scattering techniques. We investigate techniques to provide a progressive...... interactive appearance prediction tool for this type of medium. Our renderings are validated by qualitative and quantitative comparison with photographs. Visual comparisons using our interactive tool enable us to estimate the apple particle concentration of a photographed apple juice....

  3. Modular composition predicts kinase/substrate interactions

    Directory of Open Access Journals (Sweden)

    Tozeren Aydin

    2010-06-01

    Full Text Available Abstract Background Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs in kinase-substrate couples presented in the 2006 version of the PTM database. Results The signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy. Conclusions The method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases.

  4. Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners

    National Research Council Canada - National Science Library

    Shoemaker, Benjamin A; Panchenko, Anna R

    2007-01-01

    .... In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions...

  5. Multifaceted enrichment analysis of RNA-RNA crosstalk reveals cooperating micro-societies in human colorectal cancer.

    Science.gov (United States)

    Mazza, Tommaso; Mazzoccoli, Gianluigi; Fusilli, Caterina; Capocefalo, Daniele; Panza, Anna; Biagini, Tommaso; Castellana, Stefano; Gentile, Annamaria; De Cata, Angelo; Palumbo, Orazio; Stallone, Raffaella; Rubino, Rosa; Carella, Massimo; Piepoli, Ada

    2016-05-19

    Alterations in the balance of mRNA and microRNA (miRNA) expression profiles contribute to the onset and development of colorectal cancer. The regulatory functions of individual miRNA-gene pairs are widely acknowledged, but group effects are largely unexplored. We performed an integrative analysis of mRNA-miRNA and miRNA-miRNA interactions using high-throughput mRNA and miRNA expression profiles obtained from matched specimens of human colorectal cancer tissue and adjacent non-tumorous mucosa. This investigation resulted in a hypernetwork-based model, whose functional backbone was fulfilled by tight micro-societies of miRNAs. These proved to modulate several genes that are known to control a set of significantly enriched cancer-enhancer and cancer-protection biological processes, and that an array of upstream regulatory analyses demonstrated to be dependent on miR-145, a cell cycle and MAPK signaling cascade master regulator. In conclusion, we reveal miRNA-gene clusters and gene families with close functional relationships and highlight the role of miR-145 as potent upstream regulator of a complex RNA-RNA crosstalk, which mechanistically modulates several signaling pathways and regulatory circuits that when deranged are relevant to the changes occurring in colorectal carcinogenesis. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  6. 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.

  7. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    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.

  8. 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.

  9. Genome-wide prediction of C. elegans genetic interactions.

    Science.gov (United States)

    Zhong, Weiwei; Sternberg, Paul W

    2006-03-10

    To obtain a global view of functional interactions among genes in a metazoan genome, we computationally integrated interactome data, gene expression data, phenotype data, and functional annotation data from three model organisms-Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster-and predicted genome-wide genetic interactions in C. elegans. The resulting genetic interaction network (consisting of 18,183 interactions) provides a framework for system-level understanding of gene functions. We experimentally tested the predicted interactions for two human disease-related genes and identified 14 new modifiers.

  10. Genome-Wide Prediction of C. elegans Genetic Interactions

    OpenAIRE

    Zhong, Weiwei; Sternberg, Paul W.

    2006-01-01

    To obtain a global view of functional interactions among genes in a metazoan genome, we computationally integrated interactome data, gene expression data, phenotype data, and functional annotation data from three model organisms—Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster—and predicted genome-wide genetic interactions in C. elegans. The resulting genetic interaction network (consisting of 18,183 interactions) provides a framework for system-level understandin...

  11. 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

  12. 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 new technologies

  13. Prediction of Protein-Protein Interactions Using Protein Signature Profiling

    Institute of Scientific and Technical Information of China (English)

    Mahmood A. Mahdavi; Yen-Han Lin

    2007-01-01

    Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.

  14. Predicting Marital Happiness and Stability from Newlywed Interactions.

    Science.gov (United States)

    Gottman, John M.; Coan, James; Carrere, Sybil; Swanson, Catherine

    1998-01-01

    Marital interaction processes that are predictive of divorce or marital stability and processes that discriminate between happily and unhappily married stable couples are explored (N=130). Seven types of process models are examined, and results are discussed. Divorce and stability were predicted with 83% accuracy, and satisfaction with 80%…

  15. 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…

  16. 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

  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

    2017-03-13

    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. Support vector machine for predicting protein interactions using domain scores

    Institute of Scientific and Technical Information of China (English)

    PENG Xin-jun; WANG Yi-fei

    2009-01-01

    Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.

  19. Predicting genetic interactions with random walks on biological networks

    Directory of Open Access Journals (Sweden)

    Singh Ambuj K

    2009-01-01

    Full Text Available Abstract Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree

  20. 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.

  1. Scalable prediction of compound-protein interactions using minwise hashing.

    Science.gov (United States)

    Tabei, Yasuo; Yamanishi, Yoshihiro

    2013-01-01

    The identification of compound-protein interactions plays key roles in the drug development toward discovery of new drug leads and new therapeutic protein targets. There is therefore a strong incentive to develop new efficient methods for predicting compound-protein interactions on a genome-wide scale. In this paper we develop a novel chemogenomic method to make a scalable prediction of compound-protein interactions from heterogeneous biological data using minwise hashing. The proposed method mainly consists of two steps: 1) construction of new compact fingerprints for compound-protein pairs by an improved minwise hashing algorithm, and 2) application of a sparsity-induced classifier to the compact fingerprints. We test the proposed method on its ability to make a large-scale prediction of compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and show superior performance of the proposed method compared with the previous chemogenomic methods in terms of prediction accuracy, computational efficiency, and interpretability of the predictive model. All the previously developed methods are not computationally feasible for the full dataset consisting of about 200 millions of compound-protein pairs. The proposed method is expected to be useful for virtual screening of a huge number of compounds against many protein targets.

  2. 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

  3. Predicting human genetic interactions from cancer genome evolution.

    Directory of Open Access Journals (Sweden)

    Xiaowen Lu

    Full Text Available Synthetic Lethal (SL genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75 for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

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

    OpenAIRE

    2013-01-01

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

  5. Predicting RNA-Protein Interactions Using Only Sequence Information

    Directory of Open Access Journals (Sweden)

    Muppirala Usha K

    2011-12-01

    Full Text Available Abstract Background RNA-protein interactions (RPIs play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions. Results We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB, RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC curve of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99% of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens. Conclusions Our experiments with RPISeq demonstrate that RNA-protein interactions can be

  6. 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...

  7. 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

  8. Predicting protein-protein interactions in the post synaptic density.

    Science.gov (United States)

    Bar-shira, Ossnat; Chechik, Gal

    2013-09-01

    The post synaptic density (PSD) is a specialization of the cytoskeleton at the synaptic junction, composed of hundreds of different proteins. Characterizing the protein components of the PSD and their interactions can help elucidate the mechanism of long-term changes in synaptic plasticity, which underlie learning and memory. Unfortunately, our knowledge of the proteome and interactome of the PSD is still partial and noisy. In this study we describe a computational framework to improve the reconstruction of the PSD network. The approach is based on learning the characteristics of PSD protein interactions from a set of trusted interactions, expanding this set with data collected from large scale repositories, and then predicting novel interaction with proteins that are suspected to reside in the PSD. Using this method we obtained thirty predicted interactions, with more than half of which having supporting evidence in the literature. We discuss in details two of these new interactions, Lrrtm1 with PSD-95 and Src with Capg. The first may take part in a mechanism underlying glutamatergic dysfunction in schizophrenia. The second suggests an alternative mechanism to regulate dendritic spines maturation.

  9. Predicting online extremism, content adopters, and interaction reciprocity

    CERN Document Server

    Ferrara, Emilio; Varol, Onur; Flammini, Alessandro; Galstyan, Aram

    2016-01-01

    We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising...

  10. 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.

  11. Aspects of Prediction Accuracy in Human-structure Interaction

    DEFF Research Database (Denmark)

    Pedersen, Lars

    2009-01-01

    Structures such as grandstands in stadia and office floors in buildings are typically occupied by seated persons, and it is a challenge to predict the dynamic characteristics of these structures. This is because the structures and the seated persons interact when the structures undergo vibrations......, basically with the effect that the seated persons influence the dynamic system. The mechanism of the interaction is not well understood, and there are a number of factors that might influence the mechanism of the interaction. Through experiments with a vibrating floor carrying seated humans, the paper looks...... into the mechanism of the interaction focusing on its effect on dynamic structural properties. It is investigated to which extent factors such as posture of the seated persons and the construction type of the seat on which the persons are sitting have a bearing on the structural frequency and damping. This provides...

  12. Protein-protein interaction predictions using text mining methods.

    Science.gov (United States)

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

    2015-03-01

    It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.

  13. 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....

  14. Selective prediction of interaction sites in protein structures with THEMATICS

    Directory of Open Access Journals (Sweden)

    Murga Leonel F

    2007-04-01

    Full Text Available Abstract Background Methods are now available for the prediction of interaction sites in protein 3D structures. While many of these methods report high success rates for site prediction, often these predictions are not very selective and have low precision. Precision in site prediction is addressed using Theoretical Microscopic Titration Curves (THEMATICS, a simple computational method for the identification of active sites in enzymes. Recall and precision are measured and compared with other methods for the prediction of catalytic sites. Results Using a test set of 169 enzymes from the original Catalytic Residue Dataset (CatRes it is shown that THEMATICS can deliver precise, localised site predictions. Furthermore, adjustment of the cut-off criteria can improve the recall rates for catalytic residues with only a small sacrifice in precision. Recall rates for CatRes/CSA annotated catalytic residues are 41.1%, 50.4%, and 54.2% for Z score cut-off values of 1.00, 0.99, and 0.98, respectively. The corresponding precision rates are 19.4%, 17.9%, and 16.4%. The success rate for catalytic sites is higher, with correct or partially correct predictions for 77.5%, 85.8%, and 88.2% of the enzymes in the test set, corresponding to the same respective Z score cut-offs, if only the CatRes annotations are used as the reference set. Incorporation of additional literature annotations into the reference set gives total success rates of 89.9%, 92.9%, and 94.1%, again for corresponding cut-off values of 1.00, 0.99, and 0.98. False positive rates for a 75-protein test set are 1.95%, 2.60%, and 3.12% for Z score cut-offs of 1.00, 0.99, and 0.98, respectively. Conclusion With a preferred cut-off value of 0.99, THEMATICS achieves a high success rate of interaction site prediction, about 86% correct or partially correct using CatRes/CSA annotations only and about 93% with an expanded reference set. Success rates for catalytic residue prediction are similar to those of

  15. 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.

  16. Protein complexes predictions within protein interaction networks using genetic algorithms.

    Science.gov (United States)

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

  17. Neurodegenerative diseases: quantitative predictions of protein-RNA interactions.

    Science.gov (United States)

    Cirillo, Davide; Agostini, Federico; Klus, Petr; Marchese, Domenica; Rodriguez, Silvia; Bolognesi, Benedetta; Tartaglia, Gian Gaetano

    2013-02-01

    Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer's, and Parkinson's diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction.

  18. Chemical syntheses of inhibitory substrates of the RNA-RNA ligation reaction catalyzed by the hairpin ribozyme.

    Science.gov (United States)

    Massey, Archna P; Sigurdsson, Snorri Th

    2004-01-01

    The chemical syntheses of RNA oligomers containing modifications on the 5'-carbon of the 5'-terminal nucleoside for crystallographic and mechanistic studies of the hairpin ribozyme are reported. Phosphoramidites 4 and 8 were prepared and used in solid phase syntheses of RNA oligomers containing the sequence 5'-N'UCCUCUCC, where N' indicates either 5'-chloro-5'-deoxyguanosine or 5'-amino-5'-deoxyguanosine, respectively. A ribozyme ligation assay with the 5'-chloro- and 5'-amino-modified RNA oligomers demonstrated their inhibition of the hairpin-catalyzed RNA-RNA ligation reaction.

  19. Aqueous solubility prediction: do crystal lattice interactions help?

    Science.gov (United States)

    Salahinejad, Maryam; Le, Tu C; Winkler, David A

    2013-07-01

    Aqueous solubility is a very important physical property of small molecule drugs and drug candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a major role in drug delivery and pharmacokinetics. It is believed that crystal lattice interactions are important in solubility and that including them in solubility models should improve the accuracy of the models. We used calculated values for lattice energy and sublimation enthalpy of organic molecules as descriptors to determine whether these would improve the accuracy of the aqueous solubility models. Multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a nonlinear Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) were used to derive optimal predictive models of aqueous solubility of a large and highly diverse data set of 4558 organic compounds over a normal ambient temperature range of 20-30 °C (293-303 K). A randomly selected test set and compounds from a solubility challenge were used to estimate the predictive ability of the models. The BRANNLP method showed the best statistical results with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for training and test sets. Surprisingly, including descriptors that captured crystal lattice interactions did not significantly improve the quality of these aqueous solubility models.

  20. Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems

    Directory of Open Access Journals (Sweden)

    Debiao Meng

    2014-01-01

    Full Text Available The distributed strategy of Collaborative Optimization (CO is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM, is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

  1. 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

  2. Maternal-fetal interactions, predictive markers for preeclampsia, and programming.

    Science.gov (United States)

    Huppertz, Berthold

    2015-04-01

    During pregnancy close interactions between the maternal system and the fetal system via the placenta exist that result in a powerful crosstalk between both individuals. Looking for predictive biomarkers in maternal blood is extremely difficult because of this crosstalk as such markers may be derived from only maternal sources, only placental sources or both. In particular, the concentrations of markers derived from both sources may vary because of the huge variety of reasons and sources. During the last decade this has misled a number of scientists and clinicians who tried to decipher the sources of markers and the impact of the placenta and/or the maternal vascular system. A few examples for predictive biomarkers are presented, the placenta-specific marker placental protein 13 (PP13) and the angiogenic marker PlGF being released from both mother and placenta. Finally, a further reason why biomarkers may not be successful in predicting all cases of preeclampsia is that different causative routes lead to the development of preeclampsia. The differences in the development of preeclampsia not only explain why markers may or may not have a predictive value, but also why some mothers and/or children may display long-term effects later in life.

  3. Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

    2016-11-01

    In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.

  4. Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance

    Directory of Open Access Journals (Sweden)

    Jing Zhang

    2015-09-01

    Full Text Available Figural reasoning as an indicator of fluid intelligence and the domains of the Five Factor Model were explored as predictors of scholastic performance. A total of 836 Chinese secondary school students (406 girls from grades 7 to 11 participated. Figural reasoning, as measured by Raven’s Standard Progressive Matrices, predicted performance in Math, Chinese, and English, and also for a composite score. Among the personality domains, Openness had a positive effect on performance for all subjects after controlling for all the other variables. For Conscientiousness, the effects were smaller and only significant for Math. Neuroticism had a negative effect on Math grades. The effects of Extraversion on all grades were very small and not significant. Most importantly, hierarchical latent regression analyses indicated that all interaction effects between Openness and figural reasoning were significant, revealing a compensatory interaction. Our results further suggest that scholastic performance basically relies on the same traits through the secondary school years. However, importance is given to interaction effects between ability and personality. Implications along with limitations and suggestions for future research are discussed.

  5. Predicting Molecular Crowding Effects in Ion-RNA Interactions.

    Science.gov (United States)

    Yu, Tao; Zhu, Yuhong; He, Zhaojian; Chen, Shi-Jie

    2016-09-01

    We develop a new statistical mechanical model to predict the molecular crowding effects in ion-RNA interactions. By considering discrete distributions of the crowders, the model can treat the main crowder-induced effects, such as the competition with ions for RNA binding, changes of electrostatic interaction due to crowder-induced changes in the dielectric environment, and changes in the nonpolar hydration state of the crowder-RNA system. To enhance the computational efficiency, we sample the crowder distribution using a hybrid approach: For crowders in the close vicinity of RNA surface, we sample their discrete distributions; for crowders in the bulk solvent away from the RNA surface, we use a continuous mean-field distribution for the crowders. Moreover, using the tightly bound ion (TBI) model, we account for ion fluctuation and correlation effects in the calculation for ion-RNA interactions. Applications of the model to a variety of simple RNA structures such as RNA helices show a crowder-induced increase in free energy and decrease in ion binding. Such crowding effects tend to contribute to the destabilization of RNA structure. Further analysis indicates that these effects are associated with the crowder-ion competition in RNA binding and the effective decrease in the dielectric constant. This simple ion effect model may serve as a useful framework for modeling more realistic crowders with larger, more complex RNA structures.

  6. Exploration and prediction of interactions between methanotrophs and heterotrophs.

    Science.gov (United States)

    Stock, Michiel; Hoefman, Sven; Kerckhof, Frederiek-Maarten; Boon, Nico; De Vos, Paul; De Baets, Bernard; Heylen, Kim; Waegeman, Willem

    2013-12-01

    Methanotrophs can form the basis of a methane-driven food web on which heterotrophic microorganisms can feed. In return, these heterotrophs can stimulate growth of methanotrophs in co-culture by providing growth additives. However, only a few specific interactions are currently known. We incubated nine methanotrophs with 25 heterotrophic strains in a pairwise miniaturized co-cultivation setup. Through principal component analysis and k-means clustering, methanotrophs and heterotrophs could be grouped according to their interaction behaviour, suggesting strain-dependent methanotroph-heterotroph complementarity. Co-cultivation significantly enhanced the growth parameters of three methanotrophs. This was most pronounced for Methylomonas sp. M5, with a threefold increase in maximum density and a fourfold increase in maximum increase in density in co-culture with Cupriavidus taiwanensis LMG 19424. In contrast, co-cultivation with Methylobacterium radiotolerans LMG 2269 and Pseudomonas aeruginosa LMG 12228 inhibited growth of most methanotrophs. Functional genomic analysis suggested the importance of vitamin metabolism for co-cultivation success. The generated data set was then successfully exploited as a proof-of-principle for predictive modelling of co-culture responses based on other interactions of the same heterotrophs and methanotrophs, yielding values of the area under the receiver operating characteristic curve of 0.73 upon 50% missing values for the maximum increase in density parameter. As such, these modelling-based tools were shown to hold great promise in reducing the amount of data that needs to be generated when conducting large co-cultivation studies.

  7. Combinatorial analysis of interacting RNA molecules

    CERN Document Server

    Li, Thomas J X

    2010-01-01

    Recently several minimum free energy (MFE) folding algorithms for predicting the joint structure of two interacting RNA molecules have been proposed. Their folding targets are interaction structures, that can be represented as diagrams with two backbones drawn horizontally on top of each other such that (1) intramolecular and intermolecular bonds are noncrossing and (2) there is no "zig-zag" configuration. This paper studies joint structures with arc-length at least four in which both, interior and exterior stack-lengths are at least two (no isolated arcs). The key idea in this paper is to consider a new type of shape, based on which joint structures can be derived via symbolic enumeration. Our results imply simple asymptotic formulas for the number of joint structures with surprisingly small exponential growth rates. They are of interest in the context of designing prediction algorithms for RNA-RNA interactions.

  8. Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

  9. 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...... 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...

  10. Passing messages between biological networks to refine predicted interactions.

    Directory of Open Access Journals (Sweden)

    Kimberly Glass

    Full Text Available Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation, a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  11. Passing messages between biological networks to refine predicted interactions.

    Science.gov (United States)

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

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

    Directory of Open Access Journals (Sweden)

    Jan Mielniczuk

    2017-01-01

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

  13. 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.

  14. The interaction between stress and positive affect in predicting mortality.

    Science.gov (United States)

    Okely, Judith A; Weiss, Alexander; Gale, Catharine R

    2017-09-01

    Positive affect is associated with longevity; according to the stress-buffering hypothesis, this is because positive affect reduces the health harming effects of psychological stress. If this mechanism plays a role, then the association between positive affect and mortality risk should be most apparent among individuals who report higher stress. Here, we test this hypothesis. The sample consisted of 8542 participants aged 32-86 from the National Health and Nutrition Examination Survey (NHANES I) Epidemiological Follow-up Study (NHEFS). We used Cox's proportional hazards regression to test for the main effects of and the interaction between positive affect and perceived stress in predicting mortality risk over a 10year follow up period. Greater positive affect was associated with lower mortality risk. We found a significant interaction between positive affect and perceived stress such that the association between positive affect and mortality risk was stronger in people reporting higher stress. In the fully adjusted model, a standard deviation increase in positive affect was associated with a 16% (HR=0.84; 95% CI=0.75, 0.95) reduction in mortality risk among participants who reported high levels of stress. The association between positive affect and mortality risk was weaker and not significant among participants who reported low levels of stress (HR=0.98; 95% CI=0.89, 1.08). Our results support the stress-buffering model and illustrate that the association between positive affect and reduced risk may be strongest under challenging circumstances. Copyright © 2017. Published by Elsevier Inc.

  15. Structure-templated predictions of novel protein interactions from sequence information.

    Directory of Open Access Journals (Sweden)

    Doron Betel

    2007-09-01

    Full Text Available The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

  16. Structure-templated predictions of novel protein interactions from sequence information.

    Science.gov (United States)

    Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W V

    2007-09-01

    The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

  17. Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.

    Science.gov (United States)

    Shi, Ming-Guang; Xia, Jun-Feng; Li, Xue-Ling; Huang, De-Shuang

    2010-03-01

    Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

  18. Predicting interactions from mechanistic information: can omic data validate theories?

    Science.gov (United States)

    Borgert, Christopher J

    2007-09-01

    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.

  19. 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 ...

  20. Theoretical predictions of transverse kinematic imbalance in neutrino-nucleus interactions

    CERN Document Server

    Pickering, Luke

    2016-01-01

    Distributions of transverse kinematic imbalance in neutrino-nucleus interactions in the few GeV regime are sensitive to nuclear effects. We present a study comparing the latest predictions of transverse kinematic imbalance from the interaction simulations, NuWro and GENIE. We dis- cuss the differences between the model predictions.

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

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

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

  2. 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.

  3. Interdependence and Predictability of Human Mobility and Social Interactions

    CERN Document Server

    De Domenico, Manlio; Musolesi, Mirco

    2012-01-01

    Previous studies have shown that human movement is predictable to a certain extent at different geographic scales. Existing prediction techniques exploit only the past history of the person taken into consideration as input of the predictors. In this paper, we show that by means of multivariate nonlinear time series prediction techniques it is possible to increase the accuracy of movement forecasting by considering movements of friends or people with correlated mobility patterns (i.e., characterised by high mutual information) as input of the predictor. Finally, we evaluate the proposed techniques on the Nokia Mobile Data Challenge and Cabspotting datasets.

  4. Determining confidence of predicted interactions between HIV-1 and human proteins using conformal method.

    Science.gov (United States)

    Nouretdinov, Ilia; Gammerman, Alex; Qi, Yanjun; Klein-Seetharaman, Judith

    2012-01-01

    Identifying protein-protein interactions (PPI's) is critical for understanding virtually all cellular molecular mechanisms. Previously, predicting PPI's was treated as a binary classification task and has commonly been solved in a supervised setting which requires a positive labeled set of known PPI's and a negative labeled set of non-interacting protein pairs. In those methods, the learner provides the likelihood of the predicted interaction, but without a confidence level associated with each prediction. Here, we apply a conformal prediction framework to make predictions and estimate confidence of the predictions. The conformal predictor uses a function measuring relative 'strangeness' interacting pairs to check whether prediction of a new example added to the sequence of already known PPI's would conform to the 'exchangeability' assumption: distribution of interacting pairs is invariant with any permutations of the pairs. In fact, this is the only assumption we make about the data. Another advantage is that the user can control a number of errors by providing a desirable confidence level. This feature of CP is very useful for a ranking list of possible interactive pairs. In this paper, the conformal method has been developed to deal with just one class - class interactive proteins - while there is not clearly defined of 'non-interactive'pairs. The confidence level helps the biologist in the interpretation of the results, and better assists the choices of pairs for experimental validation. We apply the proposed conformal framework to improve the identification of interacting pairs between HIV-1 and human proteins.

  5. Predicting protein interactions via parsimonious network history inference.

    Science.gov (United States)

    Patro, Rob; Kingsford, Carl

    2013-07-01

    Reconstruction of the network-level evolutionary history of protein-protein interactions provides a principled way to relate interactions in several present-day networks. Here, we present a general framework for inferring such histories and demonstrate how it can be used to determine what interactions existed in the ancestral networks, which present-day interactions we might expect to exist based on evolutionary evidence and what information extant networks contain about the order of ancestral protein duplications. Our framework characterizes the space of likely parsimonious network histories. It results in a structure that can be used to find probabilities for a number of events associated with the histories. The framework is based on a directed hypergraph formulation of dynamic programming that we extend to enumerate many optimal and near-optimal solutions. The algorithm is applied to reconstructing ancestral interactions among bZIP transcription factors, imputing missing present-day interactions among the bZIPs and among proteins from five herpes viruses, and determining relative protein duplication order in the bZIP family. Our approach more accurately reconstructs ancestral interactions than existing approaches. In cross-validation tests, we find that our approach ranks the majority of the left-out present-day interactions among the top 2 and 17% of possible edges for the bZIP and herpes networks, respectively, making it a competitive approach for edge imputation. It also estimates relative bZIP protein duplication orders, using only interaction data and phylogenetic tree topology, which are significantly correlated with sequence-based estimates. The algorithm is implemented in C++, is open source and is available at http://www.cs.cmu.edu/ckingsf/software/parana2. Supplementary data are available at Bioinformatics online.

  6. NOXclass: prediction of protein-protein interaction types

    Directory of Open Access Journals (Sweden)

    Sommer Ingolf

    2006-01-01

    Full Text Available Abstract Background Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. Results Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. Conclusion NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.

  7. New approach for predicting protein-protein interactions

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    @@ Protein-protein interactions (PPIs) are of vital importance for virtually all processes of a living cell. The study of these associations of protein molecules could improve people's understanding of diseases and provide basis for therapeutic approaches.

  8. Ξ hypernuclei predicted by the new interaction model ESC08

    Directory of Open Access Journals (Sweden)

    Rijken Th.A.

    2010-04-01

    Full Text Available The features of the new interaction model ESC08 in ΛN , ΣN and ΞN channels are demonstrated by the partial wave contributions to single hyperon potentials UY (Y = Λ, Σ, Ξ in nuclear matter on the basis of the G-matrix theory. Ξ hypernuclei are studied with the ΞN G-matrix interactions derived from ESC08.

  9. Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier

    Directory of Open Access Journals (Sweden)

    Haijiang Geng

    2015-01-01

    Full Text Available Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC- based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.

  10. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network.

    Science.gov (United States)

    Yan, Xiao-Ying; Zhang, Shao-Wu; Zhang, Song-Yao

    2016-02-01

    The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions.

  11. SELF-BLM: Prediction of drug-target interactions via self-training SVM

    Science.gov (United States)

    Keum, Jongsoo; Nam, Hojung

    2017-01-01

    Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM. PMID:28192537

  12. Do Intelligence and Sustained Attention Interact in Predicting Academic Achievement?

    Science.gov (United States)

    Steinmayr, Ricarda; Ziegler, Mattias; Trauble, Birgit

    2010-01-01

    Research in clinical samples suggests that the relationship between intelligence and academic achievement might be moderated by sustained attention. The present study aimed to explore whether this interaction could be observed in a non-clinical sample. We investigated a sample of 11th and 12th grade students (N = 231). An overall performance score…

  13. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David

    2009-01-01

    Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used...

  14. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.

    Science.gov (United States)

    Peón, Antonio; Naulaerts, Stefan; Ballester, Pedro J

    2017-06-19

    Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC50 10 µM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.

  15. 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.

  16. 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.

  17. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    Science.gov (United States)

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  18. A modified resonant recognition model to predict protein-protein interaction

    Institute of Scientific and Technical Information of China (English)

    LIU Xiang; WANG Yifei

    2007-01-01

    Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model (RRM),and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM).The key characteristic of the new model is that it can predict directly the proteinprotein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.

  19. 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.

  20. Prediction of Protein-protein Interactions on the Basis of Evolutionary Conservation of Protein Functions

    Directory of Open Access Journals (Sweden)

    Ekaterina Kotelnikova

    2007-01-01

    Full Text Available Motivation: Although a great deal of progress is being made in the development of fast and reliable experimental techniques to extract genome-wide networks of protein-protein and protein-DNA interactions, the sequencing of new genomes proceeds at an even faster rate. That is why there is a considerable need for reliable methods of in-silico prediction of protein interaction based solely on sequence similarity information and known interactions from well-studied organisms. This problem can be solved if a dependency exists between sequence similarity and the conservation of the proteins’ functions.Results: In this paper, we introduce a novel probabilistic method for prediction of protein-protein interactions using a new empirical probabilistic formula describing the loss of interactions between homologous proteins during the course of evolution. This formula describes an evolutional process quite similar to the process of the Earth’s population growth. In addition, our method favors predictions confi rmed by several interacting pairs over predictions coming from a single interacting pair. Our approach is useful in working with “noisy” data such as those coming from high-throughput experiments. We have generated predictions for fi ve “model” organisms: H. sapiens, D. melanogaster, C. elegans, A. thaliana, and S. cerevisiae and evaluated the quality of these predictions.

  1. Macroevolutionary diversity of amniote limb proportions predicted by developmental interactions.

    Science.gov (United States)

    Young, Nathan M

    2013-11-01

    Mammals, birds, and reptiles exhibit a remarkable diversity of limb proportions. These evolved differences are thought to reflect selection for biomechanical, postural, and locomotor requirements primarily acting on independent variation in later fetal and postnatal segmental growth. However, earlier conserved developmental events also have the potential to impact the evolvability of limb proportions by limiting or biasing initial variation among segments. Notably, proximo-distal patterning of the amniote limb through activation-inhibition dynamics predicts that initial proportions of segments should exhibit both tradeoffs between stylopod and autopod and a diagnostic reduction in variance of the zeugopod. Here it is demonstrated that this developmental "design rule" predicts patterns of macroevolutionary diversity despite the effects of variation in segmental growth over ontogeny, lineage-specific differences in phylogenetic history, or functional adaptation. These results provide critical comparative evidence of a conserved Turing-like mechanism in proximo-distal limb segmentation, and suggest that development has played a previously unrecognized role in the evolvability of limb proportions in a wide range of amniote taxa.

  2. Using compound similarity and functional domain composition for prediction of drug-target interaction networks.

    Science.gov (United States)

    Chen, Lei; He, Zhi-Song; Huang, Tao; Cai, Yu-Dong

    2010-11-01

    Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.

  3. The predicted secretome of Lactobacillus plantarum WCFS1 sheds light on interactions with its environment

    NARCIS (Netherlands)

    Boekhorst, J.; Wels, M.; Kleerebezem, M.; Siezen, R.J.

    2006-01-01

    The predicted extracellular proteins of the bacterium Lactobacillus plantarum were analysed to gain insight into the mechanisms underlying interactions of this bacterium with its environment. Extracellular proteins play important roles in processes ranging from probiotic effects in the gastrointesti

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

    Directory of Open Access Journals (Sweden)

    Sepideh Babaei

    2015-05-01

    Full Text Available 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.

  5. Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

    Directory of Open Access Journals (Sweden)

    Liao Li

    2010-10-01

    Full Text Available Abstract Background Protein-protein interaction (PPI plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs, based on domains represented as interaction profile hidden Markov models (ipHMM where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB. Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD. Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure, an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on

  6. Predict drug-protein interaction in cellular networking.

    Science.gov (United States)

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment.

  7. 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

  8. A Mechanistic Modeling Framework for Predicting Metabolic Interactions in Complex Mixtures

    Science.gov (United States)

    Cheng, Shu

    2011-01-01

    Background: Computational modeling of the absorption, distribution, metabolism, and excretion of chemicals is now theoretically able to describe metabolic interactions in realistic mixtures of tens to hundreds of substances. That framework awaits validation. Objectives: Our objectives were to a) evaluate the conditions of application of such a framework, b) confront the predictions of a physiologically integrated model of benzene, toluene, ethylbenzene, and m-xylene (BTEX) interactions with observed kinetics data on these substances in mixtures and, c) assess whether improving the mechanistic description has the potential to lead to better predictions of interactions. Methods: We developed three joint models of BTEX toxicokinetics and metabolism and calibrated them using Markov chain Monte Carlo simulations and single-substance exposure data. We then checked their predictive capabilities for metabolic interactions by comparison with mixture kinetic data. Results: The simplest joint model (BTEX interacting competitively for cytochrome P450 2E1 access) gives qualitatively correct and quantitatively acceptable predictions (with at most 50% deviations from the data). More complex models with two pathways or back-competition with metabolites have the potential to further improve predictions for BTEX mixtures. Conclusions: A systems biology approach to large-scale prediction of metabolic interactions is advantageous on several counts and technically feasible. However, ways to obtain the required parameters need to be further explored. PMID:21835728

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

    Directory of Open Access Journals (Sweden)

    Jason Ernst

    2008-03-01

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

  10. Predicting the Creativity of Design Majors Based on the Interaction of Diverse Personality Traits

    Science.gov (United States)

    Chang, Chi-Cheng; Peng, Li-Pei; Lin, Ju-Sen; Liang, Chaoyun

    2015-01-01

    In this study, design majors were analysed to examine how diverse personality traits interact and influence student creativity. The study participants comprised 476 design majors. The results indicated that openness predicted the originality of creativity, whereas openness, conscientiousness and agreeableness predicted the usefulness of…

  11. Predicting the Creativity of Design Majors Based on the Interaction of Diverse Personality Traits

    Science.gov (United States)

    Chang, Chi-Cheng; Peng, Li-Pei; Lin, Ju-Sen; Liang, Chaoyun

    2015-01-01

    In this study, design majors were analysed to examine how diverse personality traits interact and influence student creativity. The study participants comprised 476 design majors. The results indicated that openness predicted the originality of creativity, whereas openness, conscientiousness and agreeableness predicted the usefulness of…

  12. Drug-target interaction prediction by random walk on the heterogeneous network.

    Science.gov (United States)

    Chen, Xing; Liu, Ming-Xi; Yan, Gui-Ying

    2012-07-01

    Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.

  13. Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets

    NARCIS (Netherlands)

    Lu, X.; Kensche, P.R.; Huynen, M.A.; Notebaart, R.A.

    2013-01-01

    Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate

  14. 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

    2014-07-01

    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. © The Author(s) 2013.

  15. 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...

  16. A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2008-09-01

    Full Text Available Abstract Background The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage. Results Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy. In particular, it handles the high false positive rates of current interaction data well. Conclusion The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available.

  17. Predicting drug-target interactions through integrative analysis of chemogenetic assays in yeast.

    Science.gov (United States)

    Heiskanen, Marja A; Aittokallio, Tero

    2013-04-05

    Chemical-genomic and genetic interaction profiling approaches are widely used to study mechanisms of drug action and resistance. However, there exist a number of scoring algorithms customized to different experimental assays, the relative performance of which remains poorly understood, especially with respect to different types of chemogenetic assays. Using yeast Saccharomyces cerevisiae as a test bed, we carried out a systematic evaluation among the main drug target analysis approaches in terms of predicting global drug-target interaction networks. We found drastic differences in their performance across different chemical-genomic assay types, such as those based on heterozygous and homozygous diploid or haploid deletion mutant libraries. Moreover, a relatively small overlap in the predicted targets was observed between those approaches that use either chemical-genomic screening alone or combined with genetic interaction profiling. A rank-based integration of the complementary scoring approaches led to improved overall performance, demonstrating that genetic interaction profiling provides added information on drug target prediction. Optimal performance was achieved when focusing specifically on the negative tail of the genetic interactions, suggesting that combining synthetic lethal interactions with chemical-genetic interactions provides highest information on drug-target interactions. A network view of rapamycin-interacting genes, pathways and complexes was used as an example to demonstrate the benefits of such integrated and optimized analysis of chemogenetic assays in yeast.

  18. Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI to Alzheimer's Disease (AD conversion.

    Directory of Open Access Journals (Sweden)

    Han Li

    Full Text Available Identifying patients with Mild Cognitive Impairment (MCI who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features.

  19. 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.

  20. Physiologically Based Pharmacokinetic Modeling Framework for Quantitative Prediction of an Herb–Drug Interaction

    Science.gov (United States)

    Brantley, S J; Gufford, B T; Dua, R; Fediuk, D J; Graf, T N; Scarlett, Y V; Frederick, K S; Fisher, M B; Oberlies, N H; Paine, M F

    2014-01-01

    Herb–drug interaction predictions remain challenging. Physiologically based pharmacokinetic (PBPK) modeling was used to improve prediction accuracy of potential herb–drug interactions using the semipurified milk thistle preparation, silibinin, as an exemplar herbal product. Interactions between silibinin constituents and the probe substrates warfarin (CYP2C9) and midazolam (CYP3A) were simulated. A low silibinin dose (160 mg/day × 14 days) was predicted to increase midazolam area under the curve (AUC) by 1%, which was corroborated with external data; a higher dose (1,650 mg/day × 7 days) was predicted to increase midazolam and (S)-warfarin AUC by 5% and 4%, respectively. A proof-of-concept clinical study confirmed minimal interaction between high-dose silibinin and both midazolam and (S)-warfarin (9 and 13% increase in AUC, respectively). Unexpectedly, (R)-warfarin AUC decreased (by 15%), but this is unlikely to be clinically important. Application of this PBPK modeling framework to other herb–drug interactions could facilitate development of guidelines for quantitative prediction of clinically relevant interactions. PMID:24670388

  1. Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

    Science.gov (United States)

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

  2. Proteome-wide prediction of self-interacting proteins based on multiple properties.

    Science.gov (United States)

    Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

    2013-06-01

    Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This

  3. 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.

  4. 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.

  5. Interaction features for prediction of perceptual segmentation: Effects of musicianship and experimental task

    DEFF Research Database (Denmark)

    Hartmann, Martin; Lartillot, Olivier; Toiviainen, Petri

    2016-01-01

    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......’ segmentation yielded lower prediction rates, and involved more features for prediction, particularly more interaction features; also non-musicians required a larger time shift for optimal segmentation modelling. Prediction of the annotation task exhibited higher rates, and involved more musical features than...... for the real-time task; in addition, the real-time task required time shifting of the segmentation data for its optimal modelling. We also found that annotation task models that were weighted according to boundary strength ratings exhibited improvements in segmentation prediction rates and involved more...

  6. 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.

  7. 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.

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

    Science.gov (United States)

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

    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.

  9. 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.

  10. Predicting clinical relevance of grapefruit-drug interactions: a complicated process.

    Science.gov (United States)

    Bailey, D G

    2017-04-01

    Grapefruit juice interacts with a number of drugs. This commentary provides feedback on a previously proposed approach for predicting clinically relevant interactions with grapefruit juice based on the average inherent oral bioavailability (F) and magnitude of increase in bioavailability with other CYP3A inhibitors of the drug. Additional factors such as variability of the magnitude of the pharmacokinetic interaction among individuals, product monograph cautionary statements and vulnerability of the patient population should be considered. A flow diagram is provided that should improve prediction of the pharmacokinetic interaction and clinical relevance for affected drugs and that recommends different courses of action for patient management. Forecasting the clinical importance of a particular drug interaction with grapefruit can be improved through consideration of additional readily available drug regulatory information. © 2016 John Wiley & Sons Ltd.

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

    Directory of Open Access Journals (Sweden)

    Lukas Windhager

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

  12. 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

  13. 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/.

  14. Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs

    Directory of Open Access Journals (Sweden)

    Tozeren Aydin

    2009-05-01

    Full Text Available Abstract Background Host protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology favors excessive amounts of virus production in a stressed host cell network. Short linear peptide motifs common to both virus and host provide the basis for host network modification. Methods We focused our host-pathogen study on the binding and competing interactions of HIV-1 and human proteins. We showed that peptide motifs conserved across 70% of HIV-1 subtype B and C samples occurred in similar positions on HIV-1 proteins, and we documented protein domains that interact with these conserved motifs. We predicted which human proteins may be targeted by HIV-1 by taking pairs of human proteins that may interact via a motif conserved in HIV-1 and the corresponding interacting protein domain. Results Our predictions were enriched with host proteins known to interact with HIV-1 proteins ENV, NEF, and TAT (p-value Conclusion A list of host proteins highly enriched with those targeted by HIV-1 proteins can be obtained by searching for host protein motifs along virus protein sequences. The resulting set of host proteins predicted to be targeted by virus proteins will become more accurate with better annotations of motifs and domains. Nevertheless, our study validates the role of linear binding motifs shared by virus and host proteins as an important part of the crosstalk between virus and host.

  15. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps.

    Science.gov (United States)

    Nabieva, Elena; Jim, Kam; Agarwal, Amit; Chazelle, Bernard; Singh, Mona

    2005-06-01

    Determining protein function is one of the most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. We develop a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks. http://compbio.cs.princeton.edu/function

  16. Learning to Predict Links by Integrating Structure and Interaction Information in Microblogs

    Institute of Scientific and Technical Information of China (English)

    贾岩涛; 王元卓; 程学旗

    2015-01-01

    Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.

  17. 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.

  18. Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods.

    Science.gov (United States)

    Srivastava, A; Mazzocco, G; Kel, A; Wyrwicz, L S; Plewczynski, D

    2016-03-01

    Protein-protein interactions (PPIs) play a vital role in most biological processes. Hence their comprehension can promote a better understanding of the mechanisms underlying living systems. However, besides the cost and the time limitation involved in the detection of experimentally validated PPIs, the noise in the data is still an important issue to overcome. In the last decade several in silico PPI prediction methods using both structural and genomic information were developed for this purpose. Here we introduce a unique validation approach aimed to collect reliable non interacting proteins (NIPs). Thereafter the most relevant protein/protein-pair related features were selected. Finally, the prepared dataset was used for PPI classification, leveraging the prediction capabilities of well-established machine learning methods. Our best classification procedure displayed specificity and sensitivity values of 96.33% and 98.02%, respectively, surpassing the prediction capabilities of other methods, including those trained on gold standard datasets. We showed that the PPI/NIP predictive performances can be considerably improved by focusing on data preparation.

  19. 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.

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

    Science.gov (United States)

    Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

    2015-01-01

    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).

  1. Prediction of ship-ship interactions in ports by a non-hydrostatic model

    Institute of Scientific and Technical Information of China (English)

    周明贵; 邹早建

    2015-01-01

    Complicated channel geometry and currents may aggravate the interactions between passing ships and berthed ships, which should be evaluated and taken into account in a port design. A method for predicting the ship-ship interactions, based on a non-hydrostatic shallow water flow model, is presented in this paper and is validated by comparing the numerical results with experimental data. The method is subsequently applied to predict the interaction forces acting on a berthed ship due to a passing ship in ports. The influences of the difference of the water depths between the dock and the main channel, the dock geometry, the current and another berthed ship in the dock on the ship-ship interactions are studied. Analysis based on the numerical results is carried out, which is useful for the port design.

  2. 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.

  3. Drug-target interaction prediction: databases, web servers and computational models.

    Science.gov (United States)

    Chen, Xing; Yan, Chenggang Clarence; Zhang, Xiaotian; Zhang, Xu; Dai, Feng; Yin, Jian; Zhang, Yongdong

    2016-07-01

    Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

  4. 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.

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  7. PAIRpred: partner-specific prediction of interacting residues from sequence and structure.

    Science.gov (United States)

    Minhas, Fayyaz ul Amir Afsar; Geiss, Brian J; Ben-Hur, Asa

    2014-07-01

    We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/. © 2013 Wiley Periodicals, Inc.

  8. 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

  9. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Kyunghyun Park

    Full Text Available As pharmacodynamic drug-drug interactions (PD DDIs could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

  10. 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.

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

    NARCIS (Netherlands)

    Babaei, S.; Mahfouz, A.M.E.T.A.; Hulsman, M.; Lelieveldt, B.P.F.; De Ridder, J.; Reinders, M.J.T.

    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 frequen

  13. Genetic interactions for heat stress and production level: predicting foreign from domestic data

    Science.gov (United States)

    Genetic by environmental interactions were estimated from U.S. national data by separately adding random regressions for heat stress (HS) and herd production level (HL) to the all-breed animal model to improve predictions of future records and rankings in other climate and production situations. Yie...

  14. 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.…

  15. Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms.

    Science.gov (United States)

    Kurubanjerdjit, Nilubon; Huang, Chien-Hung; Lee, Yu-Liang; Tsai, Jeffrey J P; Ng, Ka-Lok

    2013-11-01

    MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states. A lot of research has tried to discover microRNAs and target gene interactions by implementing machine learning classifiers with target prediction algorithms. However, high rates of false positives have been reported as a result of undetermined factors which will affect recognition. Therefore, integrating diverse techniques could improve the prediction. In this paper we propose identifying microRNAs target of Arabidopsis thaliana by integrating prediction scores from PITA, miRanda and RNAHybrid algorithms used as a feature vector of microRNA-target interactions, and then implementing SVM, random forest tree and neural network machine learning algorithms to make final predictions by majority voting. Furthermore, microRNA target genes are linked with their protein-protein interaction (PPI) partners. We focus on plant resistance genes and transcription factor information to provide new insights into plant pathogen interaction networks. Downstream pathways are characterized by the Jaccard coefficient, which is implemented based on Gene Ontology. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/At_miRNA/.

  16. Developing algorithms for predicting protein-protein interactions of homology modeled proteins.

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel; Roe, Diana C.

    2006-01-01

    The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.

  17. The Frequency-Predictability Interaction in Reading: It Depends Where You're Coming from

    Science.gov (United States)

    Hand, Christopher J.; Miellet, Sebastien; O'Donnell, Patrick J.; Sereno, Sara C.

    2010-01-01

    A word's frequency of occurrence and its predictability from a prior context are key factors determining how long the eyes remain on that word in normal reading. Past reaction-time and eye movement research can be distinguished by whether these variables, when combined, produce interactive or additive results, respectively. Our study addressed…

  18. Testing Predictions of the Interactive Activation Model in Recovery from Aphasia after Treatment

    Science.gov (United States)

    Jokel, Regina; Rochon, Elizabeth; Leonard, Carol

    2004-01-01

    This paper presents preliminary results of pre- and post-treatment error analysis from an aphasic patient with anomia. The Interactive Activation (IA) model of word production (Dell, Schwartz, Martin, Saffran, & Gagnon, 1997) is utilized to make predictions about the anticipated changes on a picture naming task and to explain emerging patterns.…

  19. 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…

  20. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Science.gov (United States)

    Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

    2013-01-01

    Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research

  1. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Directory of Open Access Journals (Sweden)

    Marcio Luis Acencio

    Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved

  2. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

    CERN Document Server

    Amor, Benjamin R C; Yaliraki, Sophia N; Barahona, Mauricio

    2016-01-01

    Allosteric regulation is central to many biochemical processes. Allosteric sites provide a target to fine-tune protein activity, yet we lack computational methods to predict them. Here, we present an efficient graph-theoretical approach for identifying allosteric sites and the mediating interactions that connect them to the active site. Using an atomistic graph with edges weighted by covalent and non-covalent bond energies, we obtain a bond-to-bond propensity that quantifies the effect of instantaneous bond fluctuations propagating through the protein. We use this propensity to detect the sites and communication pathways most strongly linked to the active site, assessing their significance through quantile regression and comparison against a reference set of 100 generic proteins. We exemplify our method in detail with three well-studied allosteric proteins: caspase-1, CheY, and h-Ras, correctly predicting the location of the allosteric site and identifying key allosteric interactions. Consistent prediction of...

  3. 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

  4. White matter microstructure of attentional networks predicts attention and consciousness functional interactions.

    Science.gov (United States)

    Chica, Ana B; Thiebaut de Schotten, Michel; Bartolomeo, Paolo; Paz-Alonso, Pedro M

    2017-09-13

    Attention is considered as one of the pre-requisites of conscious perception. Phasic alerting and exogenous orienting improve conscious perception of near-threshold information through segregated brain networks. Using a multimodal neuroimaging approach, combining data from functional MRI (fMRI) and diffusion-weighted imaging (DWI), we investigated the influence of white matter properties of the ventral branch of superior longitudinal fasciculus (SLF III) in functional interactions between attentional systems and conscious perception. Results revealed that (1) reduced integrity of the left hemisphere SLF III was predictive of the neural interactions observed between exogenous orienting and conscious perception, and (2) increased integrity of the left hemisphere SLF III was predictive of the neural interactions observed between phasic alerting and conscious perception. Our results combining fMRI and DWI data demonstrate that structural properties of the white matter organization determine attentional modulations over conscious perception.

  5. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    Directory of Open Access Journals (Sweden)

    Thomas Wallach

    2013-03-01

    Full Text Available Essentially all biological processes depend on protein-protein interactions (PPIs. Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc. contributing to temporal organization of cellular physiology in an unprecedented manner.

  6. Heat tolerance predicts the importance of species interaction effects as the climate changes.

    Science.gov (United States)

    Diamond, Sarah E; Chick, Lacy; Penick, Clint A; Nichols, Lauren M; Cahan, Sara Helms; Dunn, Robert R; Ellison, Aaron M; Sanders, Nathan J; Gotelli, Nicholas J

    2017-07-01

    Few studies have quantified the relative importance of direct effects of climate change on communities versus indirect effects that are mediated thorough species interactions, and the limited evidence is conflicting. Trait-based approaches have been popular in studies of climate change, but can they be used to estimate direct versus indirect effects? At the species level, thermal tolerance is a trait that is often used to predict winners and losers under scenarios of climate change. But thermal tolerance might also inform when species interactions are likely to be important because only subsets of species will be able to exploit the available warmer climatic niche space, and competition may intensify in the remaining, compressed cooler climatic niche space. Here, we explore the relative roles of the direct effects of temperature change and indirect effects of species interactions on forest ant communities that were heated as part of a large-scale climate manipulation at high- and low-latitude sites in eastern North America. Overall, we found mixed support for the importance of negative species interactions (competition), but found that the magnitude of these interaction effects was predictable based on the heat tolerance of the focal species. Forager abundance and nest site occupancy of heat-intolerant species were more often influenced by negative interactions with other species than by direct effects of temperature. Our findings suggest that measures of species-specific heat tolerance may roughly predict when species interactions will influence responses to global climate change. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

  7. 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.

  8. The Interactive Effects of Drinking Motives, Age, and Self-Criticism in Predicting Hazardous Drinking.

    Science.gov (United States)

    Skinner, Kayla D; Veilleux, Jennifer C

    2016-08-23

    Individuals who disclose hazardous drinking often report strong motives to drink, which may occur to modulate views of the self. Investigating self-criticism tendencies in models of drinking motives may help explain who is more susceptible to drinking for internal or external reasons. As much of the research on drinking motives and alcohol use is conducted in young adult or college student samples, studying these relations in a wider age range is clearly needed. The current study examined the interactive relationship between drinking motives (internal: coping, enhancement; external: social, conformity), levels of self-criticism (internalized, comparative), and age to predict hazardous drinking. Participants (N = 427, Mage = 34.16, 54.8% female) who endorsed drinking within the last year completed an online study assessing these constructs. Results indicated internalized self-criticism and drinking to cope interacted to predict hazardous drinking for middle-aged adults. However, comparative self-criticism and conformity motives interacted to predict greater hazardous drinking for younger-aged adults. In addition, both social and conformity motives predicted less hazardous drinking for middle-aged adults high in comparative self-criticism. Interventions that target alcohol use could minimize coping motivations to drink while targeting comparative self-criticism in the context of social, and conformity motives.

  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. 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.

  11. Conflict and love: predicting newlywed marital outcomes from two interaction contexts.

    Science.gov (United States)

    Graber, Elana C; Laurenceau, Jean-Philippe; Miga, Erin; Chango, Joanna; Coan, James

    2011-08-01

    Research on marital interaction has focused primarily on couples in conflict contexts to understand better processes associated with concurrent and longitudinal outcomes such as marital stability and quality. Although this work has consistently revealed particular emotions (e.g., contempt) or behavioral sequences (e.g., demand/withdraw) predictive of later marital distress, it largely has neglected to take positive contexts into consideration. The present longitudinal study begins to address this gap in the literature by directly comparing newlywed behaviors from a conflict-resolution interaction with those from a love-paradigm interaction to predict relationship satisfaction and divorce proneness approximately 15 months later. Results showed that actor and partner negative (contempt) and positive (affection) emotions elicited in both positive (i.e., love) and negative (i.e., conflict) interaction contexts emerged as unique predictors of relationship quality and stability for both husbands and wives. Moreover, using a linear growth model, the temporal course of positive emotion during the love context, but not the conflict context, was predictive of later relationship satisfaction. Implications for future marital research and intervention are discussed.

  12. 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.

  13. The origins of the evolutionary signal used to predict protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Swapna Lakshmipuram S

    2012-12-01

    Full Text Available Abstract Background The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods, many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

  14. A systematic prediction of drug-target interactions using molecular fingerprints and protein sequences.

    Science.gov (United States)

    Huang, Yu-An; You, Zhu-Hong; Chen, Xing

    2016-11-21

    Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features.

  15. A Model for Predicting the Interindividual Variability of Drug-Drug Interactions.

    Science.gov (United States)

    Tod, M; Bourguignon, L; Bleyzac, N; Goutelle, S

    2017-03-01

    Pharmacokinetic drug-drug interactions are frequently characterized and quantified by an AUC ratio (Rauc). The typical value of the AUC ratio in case of cytochrome-mediated interactions may be predicted by several approaches, based on in vitro or in vivo data. Prediction of the interindividual variability of Rauc would help to anticipate more completely the consequences of a drug-drug interaction. We propose and evaluate a simple approach for predicting the standard deviation (sd) of Ln(Rauc), a metric close to the interindividual coefficient of variation of Rauc. First, a model was derived to link sd(Ln Rauc) with the substrate fraction metabolized by each cytochrome and the potency of the interactors, in case of induction or inhibition. Second, the parameters involved in these equations were estimated by a Bayesian hierarchical model, using the data from 56 interaction studies retrieved from the literature. Third, the model was evaluated by several metrics based on the fold prediction error (PE) of sd(Ln Rauc). The median PE was 0.998 (the ideal value is 1) and the interquartile range was 0.96-1.03. The PE was in the acceptable interval (0.5 to 2) in 52 cases out of 56. Fourth, a surface plot of sd(Ln Rauc) as a function of the characteristics of the substrate and the interactor has been built. The minimal value of sd(Ln Rauc) was about 0.08 (obtained for Rauc = 1) while the maximal value, 0.7, was obtained for interactions involving highly metabolized substrates with strong interactors.

  16. Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.

    Science.gov (United States)

    Pan, Xiaoyong; Zhu, Lin; Fan, Yong-Xian; Yan, Junchi

    2014-11-13

    Protein-RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein-RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis.

  17. A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence.

    Science.gov (United States)

    Shao, Xiaojian; Tan, Chris S H; Voss, Courtney; Li, Shawn S C; Deng, Naiyang; Bader, Gary D

    2011-02-01

    Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders. We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction. The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.

  18. The use of in vitro methods to predict in vivo pharmacokinetics and drug interactions.

    Science.gov (United States)

    Bachmann, K A; Ghosh, R

    2001-09-01

    With the dramatic change underway in the process of drug discovery and development it has become increasingly important to define, both qualitatively and quantitatively, the dispositional features of new chemical entities (NCEs) as early in the process as possible. To that end strategies have emerged that are designed to enable reasonable predictions about a NCE's absorption from the gastrointestinal tract, systemic bioavailability and likelihood for significant pre-systemic clearance, character of metabolic processing both within the gastrointestinal tract and the liver, in vivo pharmacokinetics (PK), and likelihood for clinically significant interactions with other drugs. To some extent these strategies have embraced interspecies allometric scaling in which findings in animals are extrapolated to predict outcomes in humans. However, a greater emphasis in recent years has been placed on predicting human PK and the likelihood of clinically significant drug-drug interactions for NCEs solely from in vitro experiments. These general strategies have been methodologically streamlined so that hundreds or even thousands of experiments on a given NCE can be conducted within several days. Dispositional data from these pre-clinical experiments is useful for rapidly identifying potential marketing advantages for NCEs, and for screening out those substances that should not be placed into more expensive and labor-intensive animal experiments or brought to clinical trial. The key issue in these strategies is the accuracy with which pre-clinical findings predict clinical outcomes. Based largely on retrospective analyses the current state of the art exhibits a high percentage of useful predictions. However, there are many examples in which the prediction of either human PK or clinical drug-drug interactions from pre-clinical data has failed. The reasons for inaccurate predictions are manifold, and may include the actual in vitro methodology used, inappropriate model selection, and

  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. 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.

  2. The Generalization of Attachment Representations to New Social Situations: Predicting Behavior during Initial Interactions with Strangers

    Science.gov (United States)

    Feeney, Brooke C.; Cassidy, Jude; Ramos-Marcuse, Fatima

    2008-01-01

    The idea that attachment representations are generalized to new social situations and guide behavior with unfamiliar others is central to attachment theory. However, research regarding this important theoretical postulate has been lacking in adolescence and adulthood, as most research has focused on establishing the influence of attachment representations on close relationship dynamics. Thus, the goal of this investigation was to examine the extent to which attachment representations are predictive of adolescents’ initial behavior when meeting and interacting with new peers. High school adolescents (N = 135) participated with unfamiliar peers from another school in two social support interactions that were videotaped and coded by independent observers. Results indicated that attachment representations (assessed through interview and self-report measures) were predictive of behaviors exhibited during the discussions. Theoretical implications of results and contributions to existing literature are discussed. PMID:19025297

  3. String Theory Based Predictions for Novel Collective Modes in Strongly Interacting Fermi Gases

    CERN Document Server

    Bantilan, H; Ishii, T; Lewis, W E; Romatschke, P

    2016-01-01

    Very different strongly interacting quantum systems such as Fermi gases, quark-gluon plasmas formed in high energy ion collisions and black holes studied theoretically in string theory are known to exhibit quantitatively similar damping of hydrodynamic modes. It is not known if such similarities extend beyond the hydrodynamic limit. Do non-hydrodynamic collective modes in Fermi gases with strong interactions also match those from string theory calculations? In order to answer this question, we use calculations based on string theory to make predictions for novel types of modes outside the hydrodynamic regime in trapped Fermi gases. These predictions are amenable to direct testing with current state-of-the-art cold atom experiments.

  4. Fuzzy inferencing to identify degree of interaction in the development of fault prediction models

    Directory of Open Access Journals (Sweden)

    Rinkaj Goyal

    2017-01-01

    One related objective is the identification of influential metrics in the development of fault prediction models. A fuzzy rule intrinsically represents a form of interaction between fuzzified inputs. Analysis of these rules establishes that Low and NOT (High level of inheritance based metrics significantly contributes to the F-measure estimate of the model. Further, the Lack of Cohesion of Methods (LCOM metric was found insignificant in this empirical study.

  5. 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 $\

  6. THE INTERACTION BETWEEN PATERNALISTIC LEADERSHIP AND ACHIEVEMENT GOALS IN PREDICTING ATHLETES’ SPORTSPERSONSHIP

    OpenAIRE

    Jing-Horng Lu, Frank; Hsu, Yawen

    2015-01-01

    Paternalistic leadership, which is a prevalent leadership style in business contexts in non-Western cultures, is characterized by three dimensions: authoritarianism, benevolence, and morality. The current study of 252 Taiwanese intercollegiate athletes (Mage=20.91 years) explored this leadership style in a sports setting and examined the extent to which the interaction of paternalistic leadership and achievement goals predicted athletes’ sportspersonship. Participants completed the Paterna...

  7. 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.

  8. 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

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

    Science.gov (United States)

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

    2014-04-03

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

  10. 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.

  11. 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.

  12. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.

    Directory of Open Access Journals (Sweden)

    Hua Yu

    Full Text Available In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF and Support Vector Machine (SVM. The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

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

    Directory of Open Access Journals (Sweden)

    Shun Koyabu

    2015-01-01

    Full Text Available 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.

  14. Predicting Interactions between Common Dolphins and the Pole-and-Line Tuna Fishery in the Azores

    Science.gov (United States)

    Cruz, Maria João; Menezes, Gui; Machete, Miguel; Silva, Mónica A.

    2016-01-01

    Common dolphins (Delphinus delphis) are responsible for the large majority of interactions with the pole-and-line tuna fishery in the Azores but the underlying drivers remain poorly understood. In this study we investigate the influence of various environmental and fisheries-related factors in promoting the interaction of common dolphins with this fishery and estimate the resultant catch losses. We analysed 15 years of fishery and cetacean interaction data (1998–2012) collected by observers placed aboard tuna fishing vessels. Dolphins interacted in less than 3% of the fishing events observed during the study period. The probability of dolphin interaction varied significantly between years with no evident trend over time. Generalized additive modeling results suggest that fishing duration, sea surface temperature and prey abundance in the region were the most important factors explaining common dolphin interaction. Dolphin interaction had no impact on the catches of albacore, skipjack and yellowfin tuna but resulted in significantly lower catches of bigeye tuna, with a predicted median annual loss of 13.5% in the number of fish captured. However, impact on bigeye catches varied considerably both by year and fishing area. Our work shows that rates of common dolphin interaction with the pole-and-line tuna fishery in the Azores are low and showed no signs of increase over the study period. Although overall economic impact was low, the interaction may lead to significant losses in some years. These findings emphasize the need for continued monitoring and for further research into the consequences and economic viability of potential mitigation measures. PMID:27851763

  15. Prediction of Protein-Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests.

    Science.gov (United States)

    Jia, Jianhua; Xiao, Xuan; Liu, Bingxiang

    2016-06-01

    Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF.

  16. Children's cortisol and salivary alpha-amylase interact to predict attention bias to threatening stimuli.

    Science.gov (United States)

    Ursache, Alexandra; Blair, Clancy

    2015-01-01

    Physiological responses to threat occur through both the autonomic nervous system (ANS) and the hypothalamic pituitary adrenal (HPA) axis. Activity in these systems can be measured through salivary alpha-amylase (sAA) and salivary cortisol, respectively. Theoretical work and empirical studies have suggested the importance of examining the coordination of these systems in relation to cognitive functioning and behavior problems. Less is known, however, about whether these systems interactively predict more automatic aspects of attention processing such as attention toward emotionally salient threatening stimuli. We used a dot probe task to assess attention bias toward threatening stimuli in 347 kindergarten children. Cortisol and sAA were assayed from saliva samples collected prior to children's participation in assessments on a subsequent day. Using regression analyses, we examined relations of sAA and cortisol to attention bias. Results indicate that cortisol and sAA interact in predicting attention bias. Higher levels of cortisol predicted greater bias toward threat for children who had high levels of sAA, but predicted greater bias away from threat for children who had low levels of sAA. These results suggest that greater symmetry in HPA and ANS functioning is associated with greater reliance on automatic attention processes in the face of threat.

  17. 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.

  18. Influence of band interaction on the spin prediction of superdeformed rotational bands

    Science.gov (United States)

    Liu, S. X.; Xin, X. B.; Lei, Y. A.; Zeng, J. Y.

    2000-01-01

    The influence of band interaction on the spin predictions and the J (2) pattern of superdeformed (SD) bands are investigated. To make a reliable spin prediction using the best-fit method, the transitions with significant band mixing should be excluded from the least-squares fitting. Spin predictions for 15 SD bands in the A ~150 region are made. In particular, the spin of the lowest level of the first discovered high-spin SD band 152 Dy(1) is predicted to be I 0 = 26. A two-band mixing model is used to describe the irregular behaviour of J (2) with angular momentum. Two types of J (2) patterns are discussed. For the band-crossing case, the J (2) pattern in the band-crossing region is of a V (or inverse-V) type, which has been observed in both the A ~190 and 150 regions. For the band-mixing case characterized by a relatively weak band interaction, the J (2) pattern in the band-mixing region is of a W (or inverse-W) type, which was observed only in some SD bands in the A ~150 region.

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

    Energy Technology Data Exchange (ETDEWEB)

    D’Agostino, Tommaso [Dipartimento di Fisica, Università di Palermo, Via Archirafi 36, 90123 Palermo (Italy); Solana, José Ramón [Departamento de Física Aplicada, Universidad de Cantabria, 39005 Santander (Spain); Emanuele, Antonio, E-mail: antonio.emanuele@unipa.it [Dipartimento di Fisica, Università di Palermo, Via Archirafi 36, 90123 Palermo (Italy)

    2013-10-16

    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.

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

    Directory of Open Access Journals (Sweden)

    Erik Bijleveld

    Full Text Available 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.

  1. GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs.

    Science.gov (United States)

    Zhao, Qi; Xie, Yubin; Zheng, Yueyuan; Jiang, Shuai; Liu, Wenzhong; Mu, Weiping; Liu, Zexian; Zhao, Yong; Xue, Yu; Ren, Jian

    2014-07-01

    Small ubiquitin-like modifiers (SUMOs) regulate a variety of cellular processes through two distinct mechanisms, including covalent sumoylation and non-covalent SUMO interaction. The complexity of SUMO regulations has greatly hampered the large-scale identification of SUMO substrates or interaction partners on a proteome-wide level. In this work, we developed a new tool called GPS-SUMO for the prediction of both sumoylation sites and SUMO-interaction motifs (SIMs) in proteins. To obtain an accurate performance, a new generation group-based prediction system (GPS) algorithm integrated with Particle Swarm Optimization approach was applied. By critical evaluation and comparison, GPS-SUMO was demonstrated to be substantially superior against other existing tools and methods. With the help of GPS-SUMO, it is now possible to further investigate the relationship between sumoylation and SUMO interaction processes. A web service of GPS-SUMO was implemented in PHP+JavaScript and freely available at http://sumosp.biocuckoo.org.

  2. Prediction and systematic study of protein-protein interaction networks of Leptospira interrogans

    Institute of Scientific and Technical Information of China (English)

    SUN Jingchun; XU Jinlin; CAO Jianping; LIU Qi; GUO Xiaokui; SHI Tieliu; LI Yixue

    2006-01-01

    Leptospira interrogans serovar Lai is a pathogenic bacterium that causes a spirochetal zoonosis in humans and some animals. With its complete genome sequence available, it is possible to analyze protein-protein interactions from a whole- genome standpoint. Here we combine four recently developed computational approaches (gene fusion method, gene neighbor method, phylogenetic profiles method, and operon method) to predict protein-pro- tein interaction networks of Leptospira interrogans strain Lai. Through comprehensive analysis on in- teractions among proteins of motility and chemotaxis system, signal transduction, lipopolysaccaride bio- synthesis and a series of proteins related to adhesion and invasion, we provided information for further studying on its pathogenic mechanism. In addition, we also assigned 203 previously uncharacterized proteins with possible functions based on the known functions of its interacting partners. This work is helpful for further investigating L. interrogans strain Lai.

  3. Data-driven prediction and design of bZIP coiled-coil interactions.

    Science.gov (United States)

    Potapov, Vladimir; Kaplan, Jenifer B; Keating, Amy E

    2015-02-01

    Selective dimerization of the basic-region leucine-zipper (bZIP) transcription factors presents a vivid example of how a high degree of interaction specificity can be achieved within a family of structurally similar proteins. The coiled-coil motif that mediates homo- or hetero-dimerization of the bZIP proteins has been intensively studied, and a variety of methods have been proposed to predict these interactions from sequence data. In this work, we used a large quantitative set of 4,549 bZIP coiled-coil interactions to develop a predictive model that exploits knowledge of structurally conserved residue-residue interactions in the coiled-coil motif. Our model, which expresses interaction energies as a sum of interpretable residue-pair and triplet terms, achieves a correlation with experimental binding free energies of R = 0.68 and significantly out-performs other scoring functions. To use our model in protein design applications, we devised a strategy in which synthetic peptides are built by assembling 7-residue native-protein heptad modules into new combinations. An integer linear program was used to find the optimal combination of heptads to bind selectively to a target human bZIP coiled coil, but not to target paralogs. Using this approach, we designed peptides to interact with the bZIP domains from human JUN, XBP1, ATF4 and ATF5. Testing more than 132 candidate protein complexes using a fluorescence resonance energy transfer assay confirmed the formation of tight and selective heterodimers between the designed peptides and their targets. This approach can be used to make inhibitors of native proteins, or to develop novel peptides for applications in synthetic biology or nanotechnology.

  4. Development of a protein-ligand-binding site prediction method based on interaction energy and sequence conservation.

    Science.gov (United States)

    Tsujikawa, Hiroto; Sato, Kenta; Wei, Cao; Saad, Gul; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

    2016-09-01

    We present a new method for predicting protein-ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction.

  5. Prediction of Protein-Peptide Interactions: Application of the XPairIT to Anthrax Lethal Factor and Substrates

    Science.gov (United States)

    2013-09-01

    Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates by Margaret M. Hurley and...Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates Margaret M. Hurley and Michael S. Sellers Weapons and...Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates 5a. CONTRACT NUMBER ORAUW911QX-04-C

  6. SimBoost: A Read-Across Approach for Drug-Target Interaction Prediction Using Gradient Boosting Machines

    OpenAIRE

    He, Tong

    2016-01-01

    Computational prediction of the interaction between drugs and targets is a standing challenge in drug discovery. High performance on binary drug-target benchmark datasets was reported for a number of methods. A possible drawback of binary data is that missing values and non-interacting drug-target pairs are not differentiated. In this paper, we present a method called SimBoost that predicts the continuous binding affinities of drugs and targets and thus incorporates the whole interaction spec...

  7. Prediction of eye irritation from organic chemicals using membrane-interaction QSAR analysis.

    Science.gov (United States)

    Kulkarni, A; Hopfinger, A J; Osborne, R; Bruner, L H; Thompson, E D

    2001-02-01

    Eye irritation potency of a compound or mixture has traditionally been evaluated using the Draize rabbit-eye test (Draize et al., 1944). In order to aid predictions of eye irritation and to explore possible corresponding mechanisms of eye irritation, a methodology termed "membrane-interaction QSAR analysis" (MI-QSAR) has been developed (Kulkarni and Hopfinger 1999). A set of Draize eye-irritation data established by the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) (Bagley et al., 1992) was used as a structurally diverse training set in an MI-QSAR analysis. Significant QSAR models were constructed based primarily upon aqueous solvation-free energy of the solute and the strength of solute binding to a model phospholipid (DMPC) monolayer. The results demonstrate that inclusion of parameters to model membrane interactions of potentially irritating chemicals provides significantly better predictions of eye irritation for structurally diverse compounds than does modeling based solely on physiochemical properties of chemicals. The specific MI-QSAR models reported here are, in fact, close to the upper limit in both significance and robustness that can be expected for the variability inherent to the eye-irritation scores of the ECETOC training set. The MI-QSAR models can be used with high reliability to classify compounds of low- and high-predicted eye irritation scores. Thus, the models offer the opportunity to reduce animal testing for compounds predicted to fall into these two extreme eye-irritation score sets. The MI-QSAR paradigm may also be applicable to other toxicological endpoints, such as skin irritation, where interactions with cellular membranes are likely.

  8. Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity.

    Directory of Open Access Journals (Sweden)

    Kejian Wang

    Full Text Available Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI. However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap. Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1 some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2 in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.

  9. Integrating protein-protein interactions and text mining for protein function prediction

    Directory of Open Access Journals (Sweden)

    Leser Ulf

    2008-07-01

    Full Text Available Abstract Background Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature. Results Using this procedure, more than 80% of the GO annotations for proteins with highly conserved orthologs that are available in UniProtKb/Swiss-Prot could be verified automatically. For a subset of proteins we predicted new GO annotations that were not available in UniProtKb/Swiss-Prot. All predictions were correct (100% precision according to the verifications from a trained curator. Conclusion Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs.

  10. A Novel Feature Extraction Scheme with Ensemble Coding for Protein–Protein Interaction Prediction

    Directory of Open Access Journals (Sweden)

    Xiuquan Du

    2014-07-01

    Full Text Available Protein–protein interactions (PPIs play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF algorithm with the ensemble coding (EC method. To reduce computational time, a feature selection method (DX was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp.

  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. Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

    Science.gov (United States)

    Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

    2015-12-15

    Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

  13. Predicting drug-target interactions by dual-network integrated logistic matrix factorization

    Science.gov (United States)

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

    2017-01-01

    In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

  14. Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions.

    Directory of Open Access Journals (Sweden)

    Jon D Duke

    Full Text Available Drug-drug interactions (DDIs are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69; loratadine and alprazolam (RR = 1.86; loratadine and duloxetine (RR = 1.94; loratadine and ropinirole (RR = 3.21; and promethazine and tegaserod (RR = 3.00. When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms.

  15. Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Chuanhua Xing

    2011-07-01

    Full Text Available Protein-protein interactions (PPIs are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL, to lower the misclassification rate (both false positives and negatives through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.

  16. Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

    Science.gov (United States)

    Xing, Chuanhua; Dunson, David B

    2011-07-01

    Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.

  17. 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

  18. Modelling plant interspecific interactions from experiments of perennial crop mixtures to predict optimal combinations.

    Science.gov (United States)

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

    2017-07-28

    The contribution of plant species richness to productivity and ecosystem functioning is a long standing issue in Ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modelling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modelled 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 coefficientsfrom, 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 modelling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  19. Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping.

    Science.gov (United States)

    Scarabelli, Guido; Morra, Giulia; Colombo, Giorgio

    2010-05-19

    An increasing number of functional studies of proteins have shown that sequence and structural similarities alone may not be sufficient for reliable prediction of their interaction properties. This is particularly true for proteins recognizing specific antibodies, where the prediction of antibody-binding sites, called epitopes, has proven challenging. The antibody-binding properties of an antigen depend on its structure and related dynamics. Aiming to predict the antibody-binding regions of a protein, we investigate a new approach based on the integrated analysis of the dynamical and energetic properties of antigens, to identify nonoptimized, low-intensity energetic interaction networks in the protein structure isolated in solution. The method is based on the idea that recognition sites may correspond to localized regions with low-intensity energetic couplings with the rest of the protein, which allows them to undergo conformational changes, to be recognized by a binding partner, and to tolerate mutations with minimal energetic expense. Upon analyzing the results on isolated proteins and benchmarking against antibody complexes, it is found that the method successfully identifies binding sites located on the protein surface that are accessible to putative binding partners. The combination of dynamics and energetics can thus discriminate between epitopes and other substructures based only on physical properties. We discuss implications for vaccine design.

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

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  1. DBD-Hunter: a knowledge-based method for the prediction of DNA-protein interactions.

    Science.gov (United States)

    Gao, Mu; Skolnick, Jeffrey

    2008-07-01

    The structures of DNA-protein complexes have illuminated the diversity of DNA-protein binding mechanisms shown by different protein families. This lack of generality could pose a great challenge for predicting DNA-protein interactions. To address this issue, we have developed a knowledge-based method, DNA-binding Domain Hunter (DBD-Hunter), for identifying DNA-binding proteins and associated binding sites. The method combines structural comparison and the evaluation of a statistical potential, which we derive to describe interactions between DNA base pairs and protein residues. We demonstrate that DBD-Hunter is an accurate method for predicting DNA-binding function of proteins, and that DNA-binding protein residues can be reliably inferred from the corresponding templates if identified. In benchmark tests on approximately 4000 proteins, our method achieved an accuracy of 98% and a precision of 84%, which significantly outperforms three previous methods. We further validate the method on DNA-binding protein structures determined in DNA-free (apo) state. We show that the accuracy of our method is only slightly affected on apo-structures compared to the performance on holo-structures cocrystallized with DNA. Finally, we apply the method to approximately 1700 structural genomics targets and predict that 37 targets with previously unknown function are likely to be DNA-binding proteins. DBD-Hunter is freely available at http://cssb.biology.gatech.edu/skolnick/webservice/DBD-Hunter/.

  2. 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.

  3. 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.

  4. 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.

  5. Prediction

    CERN Document Server

    Sornette, Didier

    2010-01-01

    This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...

  6. Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.

    Science.gov (United States)

    Gao, Yu-Fei; Chen, Lei; Cai, Yu-Dong; Feng, Kai-Yan; Huang, Tao; Jiang, Yang

    2012-01-01

    Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.

  7. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

    Science.gov (United States)

    Amor, B. R. C.; Schaub, M. T.; Yaliraki, S. N.; Barahona, M.

    2016-08-01

    Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.

  8. 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.

  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. 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

  11. Predicting potential responses to future climate in an alpine ungulate: interspecific interactions exceed climate effects.

    Science.gov (United States)

    Mason, Tom H E; Stephens, Philip A; Apollonio, Marco; Willis, Stephen G

    2014-12-01

    The altitudinal shifts of many montane populations are lagging behind climate change. Understanding habitual, daily behavioural rhythms, and their climatic and environmental influences, could shed light on the constraints on long-term upslope range-shifts. In addition, behavioural rhythms can be affected by interspecific interactions, which can ameliorate or exacerbate climate-driven effects on ecology. Here, we investigate the relative influences of ambient temperature and an interaction with domestic sheep (Ovis aries) on the altitude use and activity budgets of a mountain ungulate, the Alpine chamois (Rupicapra rupicapra). Chamois moved upslope when it was hotter but this effect was modest compared to that of the presence of sheep, to which they reacted by moving 89-103 m upslope, into an entirely novel altitudinal range. Across the European Alps, a range-shift of this magnitude corresponds to a 46% decrease in the availability of suitable foraging habitat. This highlights the importance of understanding how factors such as competition and disturbance shape a given species' realised niche when predicting potential future responses to change. Furthermore, it exposes the potential for manipulations of species interactions to ameliorate the impacts of climate change, in this case by the careful management of livestock. Such manipulations could be particularly appropriate for species where competition or disturbance already strongly restricts their available niche. Our results also reveal the potential role of behavioural flexibility in responses to climate change. Chamois reduced their activity when it was warmer, which could explain their modest altitudinal migrations. Considering this behavioural flexibility, our model predicts a small 15-30 m upslope shift by 2100 in response to climate change, less than 4% of the altitudinal shift that would be predicted using a traditional species distribution model-type approach (SDM), which assumes that species' behaviour

  12. 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.

  13. The effects of reading comprehension and launch site on frequency-predictability interactions during paragraph reading.

    Science.gov (United States)

    Whitford, Veronica; Titone, Debra

    2014-01-01

    We used eye movement measures of paragraph reading to examine whether word frequency and predictability interact during the earliest stages of lexical processing, with a specific focus on whether these effects are modulated by individual differences in reading comprehension or launch site (i.e., saccade length between the prior and currently fixated word--a proxy for the amount of parafoveal word processing). The joint impact of frequency and predictability on reading will elucidate whether these variables additively or multiplicatively affect the earliest stages of lexical access, which, in turn, has implications for computational models of eye movements during reading. Linear mixed effects models revealed additive effects during both early- and late-stage reading, where predictability effects were comparable for low- and high-frequency words. Moreover, less cautious readers (e.g., readers who engaged in skimming, scanning, mindless reading) demonstrated smaller frequency effects than more cautious readers. Taken together, our findings suggest that during extended reading, frequency and predictability exert additive influences on lexical and postlexical processing, and that individual differences in reading comprehension modulate sensitivity to the effects of word frequency.

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

    Science.gov (United States)

    Orue, Izaskun; Calvete, Esther

    2016-07-19

    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.

  15. Illegitimacy and identity threat in (inter)action: predicting intergroup orientations among minority group members.

    Science.gov (United States)

    Livingstone, Andrew G; Spears, Russell; Manstead, Antony S R; Bruder, Martin

    2009-12-01

    We test the hypothesis that intergroup orientations among minority group members are shaped by the interaction between the perceived illegitimacy of intergroup relations and identity threat appraisals, as well as their main effects. This is because together they serve to focus emotion-mediated reactions on the out-group's role in threatening in-group identity. In a large-scale field study (N=646), conducted among the Welsh minority in the UK, we quasi-manipulated the extent to which Welsh identity was dependent on the 'threatened' Welsh language. Results supported our hypothesis that the illegitimacy x identity threat interaction would be strongest where Welsh identity was most dependent upon the Welsh language, and through intergroup anger would predict support for more radical, unconstitutional forms of action.

  16. Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks

    CERN Document Server

    Barthélemy, Johan; Collier, Louise; Hallet, Vincent; Moriamé, Marie; Sartenaer, Annick

    2016-01-01

    Groundwater and rock are intensively exploited in the world. When a quarry is deepened the water table of the exploited geological formation might be reached. A dewatering system is therefore installed so that the quarry activities can continue, possibly impacting the nearby water catchments. In order to recommend an adequate feasibility study before deepening a quarry, we propose two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. The levels of each index (low, medium, high, very high) correspond to the potential impact of the quarry on the regional hydrogeology. The first index is based on a discrete choice modelling methodology while the second is relying on an artificial neural network. It is shown that these two complementary approaches (the former being probabilistic while the latter fully deterministic) are able to predict accurately the level of interaction. Their use is finally illustrate...

  17. Phencyclidine in the social interaction test: an animal model of schizophrenia with face and predictive validity.

    Science.gov (United States)

    Sams-Dodd, F

    1999-01-01

    Phencyclidine (PCP) is a hallucinogenic drug that can mimic several aspects of the schizophrenic symptomatology in healthy volunteers. In a series of studies PCP was administered to rats to determine whether it was possible to develop an animal model of the positive and negative symptoms of schizophrenia. The rats were tested in the social interaction test and it was found that PCP dose-dependently induces stereotyped behaviour and social withdrawal, which may correspond to certain aspects of the positive and negative symptoms, respectively. The effects of PCP could be reduced selectively by antipsychotic drug treatment, whereas drugs lacking antipsychotic effects did not alleviate the PCP-induced behaviours. Together these findings indicate that PCP effects in the rat social interaction test may be a model of the positive and negative symptoms of schizophrenia with face and predictive validity and that it may be useful for the evaluation of novel antipsychotic compounds.

  18. Advances in CFD Prediction of Shock Wave Turbulent Boundary Layer Interactions

    Science.gov (United States)

    2006-01-01

    8◦. The wall is adiabatic. Experimental data of Deleuze [103] and Laurent [104] is available. The flow conditions are summarized in Table 10. The...Eddy Simulation of Shock Boundary Layer Interaction. In Third AFOSR International Conference on DNS and LES, Arlington, TX, August 2001. [103] Deleuze J...Conditions Reference Data M∞ Reδ × 10−4 Garnier et al[101, 102] LES 2.3 6.0 Deleuze [103], Laurent[104] E 2.3 6.0 Advances in CFD Prediction of Shock

  19. Predicting the glass transition temperature as function of crosslink density and polymer interactions in rubber compounds

    Science.gov (United States)

    D'Escamard, Gabriella; De Rosa, Claudio; Auriemma, Finizia

    2016-05-01

    Crosslink sulfur density in rubber compounds and interactions in polymer blends are two of the composition elements that affect the rubber compound properties and glass transition temperature (Tg), which is a marker of polymer properties related to its applications. Natural rubber (NR), butadiene rubber (BR) and styrene-butadiene rubber (SBR) compounds were investigated using calorimetry (DSC) and dynamic mechanical analysis (DMA). The results indicate that the Di Marzio's and Schneider's Models predict with accuracy the dependence of Tg on crosslink density and composition in miscible blends, respectively, and that the two model may represent the base to study the relevant "in service" properties of real rubber compounds.

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

    Science.gov (United States)

    2017-06-08

    AbdulHameed, Kamal Kumar, Xueping Yuˆ, Anders Wallqvist* and Jaques Reifman Abstract Background: The expanded use of multiple drugs has increased the...induced effects in humans, we can also apply this method to predict ADRs caused by individual drugs. In the present study, we expanded this method to...drug-drug interactions. Trends Pharmacol Sci . 2013;34(3):178–84. doi:10.1016/j.tips.2013.01.006. 7. Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R

  1. 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.

  2. 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.

  3. 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.

  4. Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon.

    Science.gov (United States)

    Feinauer, Christoph; Szurmant, Hendrik; Weigt, Martin; Pagnani, Andrea

    2016-01-01

    Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.

  5. Neurophysiological Processing of Emotion and Parenting Interact to Predict Inhibited Behavior: An Affective-Motivational Framework

    Directory of Open Access Journals (Sweden)

    Ellen M Kessel

    2013-07-01

    Full Text Available Although inhibited behavior problems are prevalent in childhood, relatively little is known about the intrinsic and extrinsic factors that predict a child’s ability to regulate inhibited behavior during fear- and anxiety-provoking tasks. Inhibited behavior may be linked to both disruptions in avoidance-related processing of aversive stimuli and in approach-related processing of appetitive stimuli, but previous findings are contradictory and rarely integrate consideration of the socialization context. The current exploratory study used a novel combination of neurophysiological and observation-based methods to examine whether a neurophysiological measure sensitive to approach- and avoidance-oriented emotional processing, the late positive potential (LPP, interacted with observed approach- (promotion and avoidance- (prevention oriented parenting practices to predict children’s observed inhibited behavior. Participants were 5- to 7-year-old (N = 32 typically-developing children (M = 75.72 months, SD = 6.01. Electroencephalography was continuously recorded while children viewed aversive, appetitive, or neutral images, and the LPP was generated to each picture type separately. Promotion and prevention parenting were observed during an emotional challenge with the child. Child inhibited behavior was observed during a fear and a social evaluation task. As predicted, larger LPPs to aversive images predicted more inhibited behavior during both tasks, but only when parents demonstrated low promotion. In contrast, larger LPPs to appetitive images predicted less inhibited behavior during the social evaluative task, but only when parents demonstrated high promotion; children of high promotion parents showing smaller LPPs to appetitive images showed the greatest inhibition. Parent-child goodness-of-fit and the LPP as a neural biomarker for emotional processes related to inhibited behavior are discussed.

  6. A new test set for validating predictions of protein-ligand interaction.

    Science.gov (United States)

    Nissink, J Willem M; Murray, Chris; Hartshorn, Mike; Verdonk, Marcel L; Cole, Jason C; Taylor, Robin

    2002-12-01

    We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk).

  7. 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.

  8. 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.

  9. 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.

  10. PIE: an online prediction system for protein-protein interactions from text.

    Science.gov (United States)

    Kim, Sun; Shin, Soo-Yong; Lee, In-Hee; Kim, Soo-Jin; Sriram, Ram; Zhang, Byoung-Tak

    2008-07-01

    Protein-protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.

  11. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.

    Science.gov (United States)

    Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

    2014-12-01

    Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/ .

  12. 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.

  13. The interaction of borderline personality disorder symptoms and relationship satisfaction in predicting affect.

    Science.gov (United States)

    Kuhlken, Katherine; Robertson, Christopher; Benson, Jessica; Nelson-Gray, Rosemery

    2014-01-01

    Previous research has suggested that stable, marital relationships may have overall prognostic significance for individuals with borderline personality disorder (BPD); however, research focused on the impact of nonmarital, and perhaps short-term, romantic relationships is lacking. Thus, the primary goal of this study was to examine the impact of the interaction of BPD symptoms and relationship satisfaction on state negative affect in college undergraduates. It was predicted that individuals who scored higher on measures of BPD symptoms and who were in a satisfying romantic relationship would report less negative affect than comparable individuals in a less satisfying romantic relationship. Questionnaires assessing BPD symptoms, relationship satisfaction, and negative affect were administered to 111 women, the majority of whom then completed daily measures of relationship satisfaction and negative affect over a 2-week follow-up period. Hierarchical multiple regression and hierarchical linear modeling were used to test the hypotheses. The interaction of BPD symptoms with relationship satisfaction was found to significantly predict anger, as measured at one time point, suggesting that satisfying romantic relationships may be a protective factor for individuals scoring high on measures of BPD symptoms with regard to anger.

  14. Real-Time Optimal Reach-Posture Prediction in a New Interactive Virtual Environment

    Institute of Scientific and Technical Information of China (English)

    Jingzhou Yang; R. Timothy Marler; Steven Beck; Karim Abdel-Malek; Joo Kim

    2006-01-01

    Human posture prediction is a key factor for the design and evaluation of workspaces, in a virtual environment using virtual humans. This work presents a new interface and virtual environment for the direct human optimized posture prediction (D-HOPP) approach to predicting realistic reach postures of digital humans, where reach postures entail the use of the torso, arms, and neck. D-HOPP is based on the contention where depending on what type of task is being completed, and human posture is governed by different human performance measures. A human performance measure is a physics-based metric, such as energy or discomfort, and serves as an objective function in an optimization formulation. The problem is formulated as a single-objective optimization (SOO) problem with a single performance measure and as multiobjective-optimization (MOO) problem with multiple combined performance measures. We use joint displacement, change in potential energy, and musculoskeletal discomfort as performance measures. D-HOPP is equipped with an extensive yet intuitive user-interface, and the results are presented in an interactive virtual environment.

  15. Soil Parameter Identification for Wheel-terrain Interaction Dynamics and Traversability Prediction

    Institute of Scientific and Technical Information of China (English)

    Suksun Hutangkabodee; Yahya Hashem Zweiri; Lakmal Dasarath Seneviratne; Kaspar Althoefer

    2006-01-01

    This paper presents a novel technique for identifying soil parameters for a wheeled vehicle traversing unknown terrain. The identified soil parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-soil interaction model based on Composite Simpson's Rule is employed for this purpose. The key soil parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust.The identified soil parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy.The use of identified soil parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.

  16. Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

    Directory of Open Access Journals (Sweden)

    Guohua Huang

    2015-01-01

    Full Text Available Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.

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

    Energy Technology Data Exchange (ETDEWEB)

    Robb, Kevin R [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Farmer, Mitchell [Argonne National Lab. (ANL), Argonne, IL (United States); Francis, Matthew W [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    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.

  18. Double trouble. Trait food craving and impulsivity interactively predict food-cue affected behavioral inhibition.

    Science.gov (United States)

    Meule, Adrian; Kübler, Andrea

    2014-08-01

    Impulsivity and food craving have both been implicated in overeating. Recent results suggest that both processes may interactively predict increased food intake. In the present study, female participants performed a Go/No-go task with pictures of high- and low-calorie foods. They were instructed to press a button in response to the respective target category, but withhold responses to the other category. Target category was switched after every other block, thereby creating blocks in which stimulus-response mapping was the same as in the previous block (nonshift blocks) and blocks in which it was reversed (shift blocks). The Food Cravings Questionnaires and the Barratt Impulsiveness Scale were used to assess trait and state food craving and attentional, motor, and nonplanning impulsivity. Participants had slower reaction times and more omission errors (OE) in high-calorie than in low-calorie blocks. Number of commission errors (CE) and OE was higher in shift blocks than in nonshift blocks. Trait impulsivity was positively correlated with CE in shift blocks while trait food craving was positively correlated with CE in high-calorie blocks. Importantly, CE in high-calorie-shift blocks were predicted by an interaction of food craving × impulsivity such that the relationship between food craving and CE was particularly strong at high levels of impulsivity, but vanished at low levels of impulsivity. Thus, impulsive reactions to high-calorie food-cues are particularly pronounced when both trait impulsivity and food craving is high, but low levels of impulsivity can compensate for high levels of trait food craving. Results support models of self-regulation which assume that interactive effects of low top-down control and strong reward sensitive, bottom-up mechanisms may determine eating-related disinhibition, ultimately leading to increased food intake.

  19. 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

  20. Prediction of atrial fibrillation recurrence after cardioversion-interaction analysis of cardiac autonomic regulation.

    Science.gov (United States)

    Seeck, A; Rademacher, W; Fischer, C; Haueisen, J; Surber, R; Voss, A

    2013-03-01

    Today atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice accounting for approximately one third of hospitalizations and accompanied with a 5 fold increased risk for ischemic stroke and a 1.5 fold increased mortality risk. The role of the cardiac regulation system in AF recurrence after electrical cardioversion (CV) is still unclear. The aim of this study was to investigate the autonomic regulation by analyzing the interaction between heart rate and blood pressure using novel methods of nonlinear interaction dynamics, namely joint symbolic dynamics (JSD) and segmented Poincaré plot analysis (SPPA). For the first time, we applied SPPA to analyze the interaction between two time series. Introducing a parameter set of two indices, one derived from JSD and one from SPPA, the linear discriminant function analysis revealed an overall accuracy of 89% (sensitivity 91.7%, specificity 86.7%) for the classification between patients with stable sinus rhythm (group SR, n = 15) and with AF recurrence (group REZ, n = 12). This study proves that the assessment of the autonomic regulation by analyzing the coupling of heart rate and systolic blood pressure provides a potential tool for the prediction of AF recurrence after CV and could aid in the adjustment of therapeutic options for patients with AF.

  1. 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…

  2. 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

  3. Computational Analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

    Science.gov (United States)

    Sarkar, Aurijit; Anderson, Kelcey C.; Kellogg, Glen E.

    2012-01-01

    AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q2 = 0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account – something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path towards further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins. PMID:22483632

  4. Predicting MDCK cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis

    Institute of Scientific and Technical Information of China (English)

    Li-li CHEN; Jia YAO; Jian-bo YANG; Jie YANG

    2005-01-01

    Aim: To use membrane-interaction quantitative structure-activity relationship analysis (MI-QSAR) to develop predictive models of partitioning of organic compounds in gastrointestinal cells. Methods: A training set of 22 structurally diverse compounds, whose apparent permeability accross cellular membranes of MadinDarby canine kidney (MDCK) cells were measured, were used to construct MIQSAR models. Molecular dynamic simulations were used to determine the explicit interaction of each test compound (solute) with a dimyristoyl-phosphatidyl-choline monolayer membrane model. An additional set of intramolecular solute descriptors were computed and considered in the trial pool of descriptors for building MI-QSAR models. The QSAR models were optimized using multidimensional linear regression fitting and the stepwise method. A test set of 8 compounds were evaluated using the MI-QSAR models as part of a validation process. Results:MI-QSAR models of the gastrointestinal absorption process were constructed.The descriptors found in the best MI-QSAR models are as follows: 1) ClogP (the logarithm of the 1-octanol/water partition coefficient); 2) EHOMO (the highest occupied molecular orbital energy); 3) Es (stretch energy); 4) PMY (the principal moment of inertia Y, the inertia along the y axis in the rectangular coordinates; 5) Ct(total connectivity); and 6) Enb (the energy of interactions between all of the nonbonded atoms). The most important descriptor in the models is ClogP. Conclusion:Permeability is not only determined by the properties of drug molecules, but is also very much influenced by the molecule-membrane interaction process.

  5. 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.

  6. Path (un)predictability of two interacting cracks in polycarbonate sheets using Digital Image Correlation

    Science.gov (United States)

    Koivisto, J.; Dalbe, M.-J.; Alava, M. J.; Santucci, S.

    2016-08-01

    Crack propagation is tracked here with Digital Image Correlation analysis in the test case of two cracks propagating in opposite directions in polycarbonate, a material with high ductility and a large Fracture Process Zone (FPZ). Depending on the initial distances between the two crack tips, one may observe different complex crack paths with in particular a regime where the two cracks repel each other prior to being attracted. We show by strain field analysis how this can be understood according to the principle of local symmetry: the propagation is to the direction where the local shear - mode KII in fracture mechanics language - is zero. Thus the interactions exhibited by the cracks arise from symmetry, from the initial geometry, and from the material properties which induce the FPZ. This complexity makes any long-range prediction of the path(s) impossible.

  7. Path (un)predictability of two interacting cracks in polycarbonate sheets using Digital Image Correlation.

    Science.gov (United States)

    Koivisto, J; Dalbe, M-J; Alava, M J; Santucci, S

    2016-01-01

    Crack propagation is tracked here with Digital Image Correlation analysis in the test case of two cracks propagating in opposite directions in polycarbonate, a material with high ductility and a large Fracture Process Zone (FPZ). Depending on the initial distances between the two crack tips, one may observe different complex crack paths with in particular a regime where the two cracks repel each other prior to being attracted. We show by strain field analysis how this can be understood according to the principle of local symmetry: the propagation is to the direction where the local shear - mode KII in fracture mechanics language - is zero. Thus the interactions exhibited by the cracks arise from symmetry, from the initial geometry, and from the material properties which induce the FPZ. This complexity makes any long-range prediction of the path(s) impossible.

  8. Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

    Directory of Open Access Journals (Sweden)

    Pedamallu Chandra Sekhar

    2010-08-01

    Full Text Available Abstract Background Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible. Methods Here we report the development of a simple open source program module (OpenPPI_predictor that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism. Results Results from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans. Availability The OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/.

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

    Science.gov (United States)

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

    2014-02-01

    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.

  10. 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.

  11. CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.

    Science.gov (United States)

    Hamanaka, Masatoshi; Taneishi, Kei; Iwata, Hiroaki; Ye, Jun; Pei, Jianguo; Hou, Jinlong; Okuno, Yasushi

    2017-01-01

    Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a support vector machine (SVM). However, the CGBVS has problems when training using more than a million datasets of CPIs since SVMs require an exponential increase in the calculation time and computer memory. To solve this problem, we propose the CGBVS-DNN, in which we use deep neural networks, a kind of deep learning technique, instead of the SVM. Deep learning does not require learning all input data at once because the network can be trained with small mini-batches. Experimental results show that the CGBVS-DNN outperformed the original CGBVS with a quarter million CPIs. Results of cross-validation show that the accuracy of the CGBVS-DNN reaches up to 98.2 % (σ<0.01) with 4 million CPIs.

  12. Predictive simulation of wind turbine wake interaction with an adaptive lattice Boltzmann method for moving boundaries

    Science.gov (United States)

    Deiterding, Ralf; Wood, Stephen L.

    2015-11-01

    Operating horizontal axis wind turbines create large-scale turbulent wake structures that affect the power output of downwind turbines considerably. The computational prediction of this phenomenon is challenging as efficient low dissipation schemes are necessary that represent the vorticity production by the moving structures accurately and are able to transport wakes without significant artificial decay over distances of several rotor diameters. We have developed the first version of a parallel adaptive lattice Boltzmann method for large eddy simulation of turbulent weakly compressible flows with embedded moving structures that considers these requirements rather naturally and enables first principle simulations of wake-turbine interaction phenomena at reasonable computational costs. The presentation will describe the employed algorithms and present relevant verification and validation computations. For instance, power and thrust coefficients of a Vestas V27 turbine are predicted within 5% of the manufacturer's specifications. Simulations of three Vestas V27-225kW turbines in triangular arrangement analyze the reduction in power production due to upstream wake generation for different inflow conditions.

  13. 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.

  14. Prediction of drug-like molecular properties: modeling cytochrome p450 interactions.

    Science.gov (United States)

    Jalaie, Mehran; Arimoto, Rieko; Gifford, Eric; Schefzick, Sabine; Waller, Chris L

    2004-01-01

    Preventing drug-drug interactions and reducing drug-related mortalities dictate cleaner and costlier medicines. The cost to bring a new drug to market has increased dramatically over the last 10 years, with post-discovery activities (preclinical and clinical) costs representing the majority of the spend. With the ever-increasing scrutiny that new drug candidates undergo in the post-discovery assessment phases, there is increasing pressure on discovery to deliver higher-quality drug candidates. Given that compound attrition in the early clinical stages can often be attributed to metabolic liabilities, it has been of great interest lately to implement predictive measures of metabolic stability/ liability in the drug design stage of discovery. The solution to this issue is wrapped in understanding the basic of the cytochrome P450 (CYP) enzymes functions and structures. Recently, experimental information on the structure of a variety of cytochrome P450 enzymes, major contributors to phase I metabolism, has become readily available. This, coupled with the availability of experimental information on substrate specificities, has lead to the development of numerous computational models (macromolecular, pharmacophore, and structure-activity) for the rationalization and prediction of CYP liabilities. A comprehensive review of these models is presented in this chapter.

  15. Local network topology in human protein interaction data predicts functional association.

    Directory of Open Access Journals (Sweden)

    Hua Li

    Full Text Available The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO and the Kyoto Encyclopedia of Genes and Genomes (KEGG as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-beta signaling pathway (P<10(-50. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

  16. A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction

    Directory of Open Access Journals (Sweden)

    Osval A. Montesinos-López

    2017-06-01

    Full Text Available There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments.

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

    Science.gov (United States)

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

    2013-01-01

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

  18. Interaction between oxytocin genotypes and early experience predicts quality of mothering and postpartum mood.

    Directory of Open Access Journals (Sweden)

    Viara Mileva-Seitz

    Full Text Available Individual differences in maternal behavior are affected by both early life experiences and oxytocin, but little is known about genetic variation in oxytocin genes and its effects on mothering. We examined two polymorphisms in the oxytocin peptide gene OXT (rs2740210 and rs4813627 and one polymorphism in the oxytocin receptor gene OXTR (rs237885 in 187 Caucasian mothers at six months postpartum. For OXT, both rs2740210 and rs4813627 significantly associated with maternal vocalizing to the infant. These polymorphisms also interacted with the quality of care mothers experienced in early life, to predict variation in maternal instrumental care and postpartum depression. However, postpartum depression did not mediate the gene-environment effects of the OXT SNPs on instrumental care. In contrast, the OXTR SNP rs237885 did not associate with maternal behavior, but it did associate with pre-natal (but not post-natal depression score. The findings illustrate the importance of variation in oxytocin genes, both alone and in interaction with early environment, as predictors of individual differences in human mothering. Furthermore, depression does not appear to have a causal role on the variation we report in instrumental care. This suggests that variation in instrumental care varies in association with a gene-early environment effect regardless of current depressive symptomatology. Finally, our findings highlight the importance of examining multiple dimensions of human maternal behavior in studies of genetic associations.

  19. 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.

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

    Science.gov (United States)

    Parker, Edith A.; Peek-Asa, Corinne

    2017-01-01

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

  1. 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.

  2. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions using Drug Structure and Protein Sequence Information.

    Science.gov (United States)

    Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

    2016-11-14

    Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drug-target interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-the-art Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

  3. Establishment of an in silico phospholipidosis prediction method using descriptors related to molecular interactions causing phospholipid-compound complex formation.

    Science.gov (United States)

    Haranosono, Yu; Nemoto, Shingo; Kurata, Masaaki; Sakaki, Hideyuki

    2016-04-01

    Although phospholipidosis (PLD) often affects drug development, there is no convenient in vitro or in vivo test system for PLD detection. In this study, we developed an in silico PLD prediction method based on the PLD-inducing mechanism. We focused on phospholipid (PL)-compound complex formation, which inhibits PL degradation by phospholipase. Thus, we used some molecular interactions, such as electrostatic interactions, hydrophobic interactions, and intermolecular forces, between PL and compounds as descriptors. First, we performed descriptor screening for intermolecular force and then developed a new in silico PLD prediction using descriptors related to molecular interactions. Based on the screening, we identified molecular refraction (MR) as a descriptor of intermolecular force. It is known that ClogP and most-basic pKa can be used for PLD prediction. Thereby, we developed an in silico prediction method using ClogP, most-basic pKa, and MR, which were related to hydrophobic interactions, electrostatic interactions, and intermolecular forces. In addition, a resampling method was used to determine the cut-off values for each descriptor. We obtained good results for 77 compounds as follows: sensitivity = 95.8%, specificity = 75.9%, and concordance = 88.3%. Although there is a concern regarding false-negative compounds for pKa calculations, this predictive ability will be adequate for PLD screening. In conclusion, the mechanism-based in silico PLD prediction method provided good prediction ability, and this method will be useful for evaluating the potential of drugs to cause PLD, particularly in the early stage of drug development, because this method only requires knowledge of the chemical structure.

  4. 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

  5. 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.

  6. Aerobic exercise interacts with neurotrophic factors to predict cognitive functioning in adolescents.

    Science.gov (United States)

    Lee, Tatia M C; Wong, Mark Lawrence; Lau, Benson Wui-Man; Lee, Jada Chia-Di; Yau, Suk-Yu; So, Kwok-Fai

    2014-01-01

    Recent findings have suggested that aerobic exercise may have a positive effect on brain functioning, in addition to its well-recognized beneficial effects on human physiology. This study confirmed the cognitive effects of aerobic exercise on the human brain. It also examined the relationships between exercise and the serum levels of neurotrophic factors (BDNF, IGI-1, and VEGF). A total of 91 healthy teens who exercised regularly participated in this study. A between-group design was adopted to compare cognitive functioning subserved by the frontal and temporal brain regions and the serum levels of neurotrophic factors between 45 regular exercisers and 46 matched controls. The exercisers performed significantly better than the controls on the frontal and temporal functioning parameters measured. This beneficial cognitive effect was region-specific because no such positive cognitive effect on task-tapping occipital functioning was observed. With respect to the serum levels of the neurotrophic factors, a negative correlation between neurotrophic factors (BDNF and VEGF) with frontal and medial-temporal lobe function was revealed. Furthermore, the levels of BDNF and VEGF interacted with exercise status in predicting frontal and temporal lobe function. This is the first report of the interaction effects of exercise and neurotrophic factors on cognitive functioning. Herein, we report preliminary evidence of the beneficial effects of regular aerobic exercise in improving cognitive functions in teens. These beneficial effects are region-specific and are associated with the serum levels of neurotrophic factors. Our findings lay the path for future studies looking at ways to translate these beneficial effects to therapeutic strategies for adolescents.

  7. 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 .

  8. 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

    2016-11-04

    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.

  9. 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.

  10. 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

  11. Geochemical simulation of fluid rock interactions to predict flowback water compostions during hydraulic fracturing

    Science.gov (United States)

    Kühn, Michael; Vieth-Hillebrand, Andrea; Wilke, Franziska D. H.

    2017-04-01

    Black shales are a heterogeneous mixture of minerals, organic matter and formation water and little is actually known about the fluid-rock interactions during hydraulic fracturing and their effects on composition of flowback and produced water. Geochemical simulations have been performed based on the analyses of "real" flowback water samples and artificial stimulation fluids from lab experiments with the aim to set up a chemical process model for shale gas reservoirs. Prediction of flowback water compositions for potential or already chosen sites requires validated and parameterized geochemical models. For the software "Geochemist's Workbench" (GWB) data bases are adapted and amended based on a literature review. Evaluation of the system has been performed in comparison with the results from laboratory experiments. Parameterization was done in regard to field data provided. Finally, reaction path models are applied for quantitative information about the mobility of compounds in specific settings. Our work leads to quantitative estimates of reservoir compounds in the flowback based on calibrations by laboratory experiments. Such information is crucial for the assessment of environmental impacts as well as to estimate human- and ecotoxicological effects of the flowback waters from a variety of natural gas shales. With a comprehensive knowledge about potential composition and mobility of flowback water, selection of water treatment techniques will become easier.

  12. 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.

  13. 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.

  14. 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.

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

    Science.gov (United States)

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

    2016-02-01

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

  16. QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

    Science.gov (United States)

    Papa, E; Kovarich, S; Gramatica, P

    2013-01-01

    The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

  17. Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients.

    Science.gov (United States)

    Porzelius, Christine; Johannes, Marc; Binder, Harald; Beissbarth, Tim

    2011-03-01

    Classification of patients based on molecular markers, for example into different risk groups, is a modern field in medical research. The aim of this classification is often a better diagnosis or individualized therapy. The search for molecular markers often utilizes extremely high-dimensional data sets (e.g. gene-expression microarrays). However, in situations where the number of measured markers (genes) is intrinsically higher than the number of available patients, standard methods from statistical learning fail to deal correctly with this so-called "curse of dimensionality". Also feature or dimension reduction techniques based on statistical models promise only limited success. Several recent methods explore ideas of how to quantify and incorporate biological prior knowledge of molecular interactions and known cellular processes into the feature selection process. This article aims to give an overview of such current methods as well as the databases, where this external knowledge can be obtained from. For illustration, two recent methods are compared in detail, a feature selection approach for support vector machines as well as a boosting approach for regression models. As a practical example, data on patients with acute lymphoblastic leukemia are considered, where the binary endpoint "relapse within first year" should be predicted. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Trauma exposure interacts with impulsivity in predicting emotion regulation and depressive mood

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    Grazia Ceschi

    2014-09-01

    Full Text Available Background: Traumatic exposure may modulate the expression of impulsive behavioral dispositions and change the implementation of emotion regulation strategies associated with depressive mood. Past studies resulted in only limited comprehension of these relationships, especially because they failed to consider impulsivity as a multifactorial construct. Objective: Based on Whiteside and Lynam's multidimensional model that identifies four distinct dispositional facets of impulsive-like behaviors, namely urgency, (lack of premeditation, (lack of perseverance, and sensation seeking (UPPS, the current study used a sample of community volunteers to investigate whether an interaction exists between impulsivity facets and lifetime trauma exposure in predicting cognitive emotion regulation and depressive mood. Methods: Ninety-three adults completed questionnaires measuring lifetime trauma exposure, impulsivity, cognitive emotion regulation, and depressive mood. Results: Results showed that trauma-exposed participants with a strong disposition toward urgency (predisposition to act rashly in intense emotional contexts tended to use fewer appropriate cognitive emotion regulation strategies than other individuals. Unexpectedly, participants lacking in perseverance (predisposition to have difficulties concentrating on demanding tasks used more appropriate emotion regulation strategies if they had experienced traumatic events during their life than if they had not. Emotion regulation mediated the path between these two impulsivity facets and depressive mood. Conclusions: Together, these findings suggest that impulsivity has a differential impact on emotion regulation and depressive mood depending on lifetime exposure to environmental factors, especially traumatic events.

  19. 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

  20. Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

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    Vijaykumar Yogesh Muley

    Full Text Available BACKGROUND: Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. METHODS: We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. CONCLUSIONS: Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling

  1. Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins.

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    Debasree Sarkar

    Full Text Available A considerable proportion of protein-protein interactions (PPIs in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs, often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME were used to identify such peptides in three cancer-associated hub proteins-MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs. These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI

  2. Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins

    Science.gov (United States)

    Sarkar, Debasree; Patra, Piya; Ghosh, Abhirupa; Saha, Sudipto

    2016-01-01

    A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins—MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators. PMID

  3. Impact of turbulence on the prediction of linear aeroacoustic interactions: Acoustic response of a turbulent shear layer

    Science.gov (United States)

    Gikadi, Jannis; Föller, Stephan; Sattelmayer, Thomas

    2014-12-01

    A powerful model to predict aeroacoustic interactions in the linear regime is the perturbed compressible linearized Navier-Stokes equations. Thus far, the frequently employed derivation suggests that the effect of turbulence and its associated Reynolds stresses is neglected and a quasi-laminar model is employed. In this paper, dynamic perturbation equations are derived incorporating the effect of turbulence and its interaction with perturbation quantities. This is done by employing a triple decomposition of the instantaneous variables. The procedure results in a closure problem for the Reynolds stresses for which a linear eddy-viscosity model is proposed. The resulting perturbation equations are applied to a grazing flow in a T-joint for which strong shear layer instabilities at certain frequencies are experimentally observed. Passive scattering properties of the grazing flow are validated against the experiments performed by Karlsson and Åbom and perturbation equations being quasi-laminar. We find that prediction models must include the effect of Reynolds stresses to capture the aeroacoustic interaction effects correctly. Neglecting its effect naturally results in the over prediction of vortex growth at the frequencies of shear layer instability and therewith in an over prediction of aeroacoustic interactions.

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

    Science.gov (United States)

    Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2009-01-26

    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. 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. 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/.

  5. 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/.

  6. Improving protein protein interaction prediction based on phylogenetic information using a least-squares support vector machine.

    Science.gov (United States)

    Craig, Roger A; Liao, Li

    2007-12-01

    Predicting protein-protein interactions has become a key step of reverse-engineering biological networks to better understand cellular functions. The experimental methods in determining protein-protein interactions are time-consuming and costly, which has motivated vigorous development of computational approaches for predicting protein-protein interactions. A set of recently developed bioinformatics methods utilizes coevolutionary information of the interacting partners (e.g., as exhibited in the form of correlations between distance matrices, where, for each protein, a matrix stores the pairwise distances between the protein and its orthologs in a group of reference genomes). We proposed a novel method to account for the intra-matrix correlations in improving predictive accuracy. The distance matrices for a pair of proteins are transformed and concatenated into a phylogenetic vector. A least-squares support vector machine is trained and tested on pairs of proteins, represented as phylogenetic vectors, whose interactions are known. The intra-matrix correlations are accounted for by introducing a weighted linear kernel, which determines the dot product of two phylogenetic vectors. The performance, measured as receiver operator characteristic (ROC) score in cross-validation experiments, shows significant improvement of our method (ROC score 0.928) over that obtained by Pearson correlations (0.659).

  7. 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.

  8. Protein-spanning water networks and implications for prediction of protein-protein interactions mediated through hydrophobic effects.

    Science.gov (United States)

    Cui, Di; Ou, Shuching; Patel, Sandeep

    2014-12-01

    Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self-assembly processes. Protein-protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein-protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy-based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue-based water density and single-linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces.

  9. Protein-lipid interactions: correlation of a predictive algorithm for lipid-binding sites with three-dimensional structural data

    Directory of Open Access Journals (Sweden)

    Goldmann Wolfgang H

    2006-03-01

    Full Text Available Abstract Background Over the past decade our laboratory has focused on understanding how soluble cytoskeleton-associated proteins interact with membranes and other lipid aggregates. Many protein domains mediating specific cell membrane interactions appear by fluorescence microscopy and other precision techniques to be partially inserted into the lipid bilayer. It is unclear whether these protein-lipid-interactions are dependent on shared protein motifs or unique regional physiochemistry, or are due to more global characteristics of the protein. Results We have developed a novel computational program that predicts a protein's lipid-binding site(s from primary sequence data. Hydrophobic labeling, Fourier transform infrared spectroscopy (FTIR, film balance, T-jump, CD spectroscopy and calorimetry experiments confirm that the interfaces predicted for several key cytoskeletal proteins (alpha-actinin, Arp2, CapZ, talin and vinculin partially insert into lipid aggregates. The validity of these predictions is supported by an analysis of the available three-dimensional structural data. The lipid interfaces predicted by our algorithm generally contain energetically favorable secondary structures (e.g., an amphipathic alpha-helix flanked by a flexible hinge or loop region, are solvent-exposed in the intact protein, and possess favorable local or global electrostatic properties. Conclusion At present, there are few reliable methods to determine the region of a protein that mediates biologically important interactions with lipids or lipid aggregates. Our matrix-based algorithm predicts lipid interaction sites that are consistent with the available biochemical and structural data. To determine whether these sites are indeed correctly identified, and whether use of the algorithm can be safely extended to other classes of proteins, will require further mapping of these sites, including genetic manipulation and/or targeted crystallography.

  10. Predicting Anatomical Therapeutic Chemical (ATC classification of drugs by integrating chemical-chemical interactions and similarities.

    Directory of Open Access Journals (Sweden)

    Lei Chen

    Full Text Available 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 2(nd-level, 3(rd-level, 4(th-level, and 5(th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.

  11. 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.

  12. 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.

  13. Struct2Net: a web service to predict protein-protein interactions using a structure-based approach.

    Science.gov (United States)

    Singh, Rohit; Park, Daniel; Xu, Jinbo; Hosur, Raghavendra; Berger, Bonnie

    2010-07-01

    Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein-protein interactions (PPIs) is a central area of interest and successful prediction would provide leads for experiments and drug design; however, the experimental coverage of the PPI interactome remains inadequate. We believe that Struct2Net is the first community-wide resource to provide structure-based PPI predictions that go beyond homology modeling. Also, most web-resources for predicting PPIs currently rely on functional genomic data (e.g. GO annotation, gene expression, cellular localization, etc.). Our structure-based approach is independent of such methods and only requires the sequence information of the proteins being queried. The web service allows multiple querying options, aimed at maximizing flexibility. For the most commonly studied organisms (fly, human and yeast), predictions have been pre-computed and can be retrieved almost instantaneously. For proteins from other species, users have the option of getting a quick-but-approximate result (using orthology over pre-computed results) or having a full-blown computation performed. The web service is freely available at http://struct2net.csail.mit.edu.

  14. Linear correlation analysis in finding interactions: Half of predicted interactions are undeterministic and one-third of candidate direct interactions are missed

    OpenAIRE

    WenJun Zhang; Xin Li

    2015-01-01

    An ecological network can be constructed by calculating the sampling data of taxon by sample type. A statistically significant Pearson linear correlation means an indirect or direct linear interaction between two taxa, and a statistically significant partial correlation based on Pearson linear correlation, due to elimination of indirect effects of other taxa, means a candidate direct interaction between two taxa. People always use Pearson linear correlation to find interactions. However, some...

  15. Heterogeneous social motives and interactions: the three predictable paths of capability development

    NARCIS (Netherlands)

    Bridoux, F.; Coeurderoy, R.; Durand, R.

    Research summary: Limited attention has been paid to the crucial role of individuals' motivation and social interactions in capability development. Building on literature in social psychology and behavioral economics that links heterogeneity in individual social motives to social interactions, we

  16. Predicting functionality of protein-DNA interactions by integrating diverse evidence

    DEFF Research Database (Denmark)

    Ucar, Duygu; Beyer, A.; Parthasarathy, S.

    2009-01-01

    Chromatin immunoprecipitation (ChIP-chip) experiments enable capturing physical interactions between regulatory proteins and DNA in vivo. However, measurement of chromatin binding alone is not sufficient to detect regulatory interactions. A detected binding event may not be biologically relevant...

  17. 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.

  18. 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

  19. Community-wide Evaluation of Methods for Predicting the Effect of Mutations on Protein-Protein Interactions

    Science.gov (United States)

    Moretti, Rocco; Fleishman, Sarel J.; Agius, Rudi; Torchala, Mieczyslaw; Bates, Paul A.; Kastritis, Panagiotis L.; Rodrigues, João P. G. L. M.; Trellet, Mikaël; Bonvin, Alexandre M. J. J.; Cui, Meng; Rooman, Marianne; Gillis, Dimitri; Dehouck, Yves; Moal, Iain; Romero-Durana, Miguel; Perez-Cano, Laura; Pallara, Chiara; Jimenez, Brian; Fernandez-Recio, Juan; Flores, Samuel; Pacella, Michael; Kilambi, Krishna Praneeth; Gray, Jeffrey J.; Popov, Petr; Grudinin, Sergei; Esquivel-Rodríguez, Juan; Kihara, Daisuke; Zhao, Nan; Korkin, Dmitry; Zhu, Xiaolei; Demerdash, Omar N. A.; Mitchell, Julie C.; Kanamori, Eiji; Tsuchiya, Yuko; Nakamura, Haruki; Lee, Hasup; Park, Hahnbeom; Seok, Chaok; Sarmiento, Jamica; Liang, Shide; Teraguchi, Shusuke; Standley, Daron M.; Shimoyama, Hiromitsu; Terashi, Genki; Takeda-Shitaka, Mayuko; Iwadate, Mitsuo; Umeyama, Hideaki; Beglov, Dmitri; Hall, David R.; Kozakov, Dima; Vajda, Sandor; Pierce, Brian G.; Hwang, Howook; Vreven, Thom; Weng, Zhiping; Huang, Yangyu; Li, Haotian; Yang, Xiufeng; Ji, Xiaofeng; Liu, Shiyong; Xiao, Yi; Zacharias, Martin; Qin, Sanbo; Zhou, Huan-Xiang; Huang, Sheng-You; Zou, Xiaoqin; Velankar, Sameer; Janin, Joël; Wodak, Shoshana J.; Baker, David

    2014-01-01

    Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side chain sampling and backbone relaxation, and evaluated packing, electrostatic and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of methodological improvement. PMID:23843247

  20. Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection.

    Science.gov (United States)

    Liu, Lili; Chen, Lei; Zhang, Yu-Hang; Wei, Lai; Cheng, Shiwen; Kong, Xiangyin; Zheng, Mingyue; Huang, Tao; Cai, Yu-Dong

    2017-02-01

    Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.

  1. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging

    Science.gov (United States)

    Hughes, Timothy J.; Kandathil, Shaun M.; Popelier, Paul L. A.

    2015-02-01

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G**, B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol-1, decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol-1.

  2. JET2 Viewer: a database of predicted multiple, possibly overlapping, protein–protein interaction sites for PDB structures

    Science.gov (United States)

    Ripoche, Hugues; Laine, Elodie; Ceres, Nicoletta; Carbone, Alessandra

    2017-01-01

    The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET2 at large-scale on more than 20 000 chains. JET2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET2 on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein–protein interactions modulation and interaction surface redesign. PMID:27899675

  3. 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

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

    Science.gov (United States)

    Holland, J Nathaniel; DeAngelis, Donald L

    2009-12-01

    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.

  5. 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.

  6. 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.

  7. Application of Hansen Solubility Parameters to predict drug-nail interactions, which can assist the design of nail medicines.

    Science.gov (United States)

    Hossin, B; Rizi, K; Murdan, S

    2016-05-01

    We hypothesised that Hansen Solubility Parameters (HSPs) can be used to predict drug-nail affinities. Our aims were to: (i) determine the HSPs (δD, δP, δH) of the nail plate, the hoof membrane (a model for the nail plate), and of the drugs terbinafine HCl, amorolfine HCl, ciclopirox olamine and efinaconazole, by measuring their swelling/solubility in organic liquids, (ii) predict nail-drug interactions by comparing drug and nail HSPs, and (iii) evaluate the accuracy of these predictions using literature reports of experimentally-determined affinities of these drugs for keratin, the main constituent of the nail plate and hoof. Many solvents caused no change in the mass of nail plates, a few solvents deswelled the nail, while others swelled the nail to varying extents. Fingernail and toenail HSPs were almost the same, while hoof HSPs were similar, except for a slightly lower δP. High nail-terbinafine HCl, nail-amorolfine HCl and nail-ciclopirox olamine affinities, and low nail-efinaconazole affinities were then predicted, and found to accurately match experimental reports of these drugs' affinities to keratin. We therefore propose that drug and nail Hansen Solubility Parameters may be used to predict drug-nail interactions, and that these results can assist in the design of drugs for the treatment of nail diseases, such as onychomycosis and psoriasis. To our knowledge, this is the first report of the application of HSPs in ungual research.

  8. 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.

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

    Institute of Scientific and Technical Information of China (English)

    Mengqu Ge; Ao Li; Minghui Wang

    2016-01-01

    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.

  10. Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops.

    Science.gov (United States)

    Bauer, A M; Hoti, F; Reetz, T C; Schuh, W-D; Léon, J; Sillanpää, M J

    2009-06-01

    In self-pollinating populations, individuals are characterized by a high degree of inbreeding. Additionally, phenotypic observations are highly influenced by genotype-by-environment interaction effects. Usually, Bayesian approaches to predict breeding values (in self-pollinating crops) omit genotype-by-environment interactions in the statistical model, which may result in biased estimates. In our study, a Bayesian Gibbs sampling algorithm was developed that is adapted to the high degree of inbreeding in self-pollinated crops and accounts for interaction effects between genotype and environment. As related lines are supposed to show similar genotype-by-environment interaction effects, an extended genetic relationship matrix is included in the Bayesian model. Additionally, since the coefficient matrix C in the mixed model equations can be characterized by rank deficiencies, the pseudoinverse of C was calculated by using the nullspace, which resulted in a faster computation time. In this study, field data of spring barley lines and data of a 'virtual' parental population of self-pollinating crops, generated by computer simulation, were used. For comparison, additional breeding values were predicted by a frequentist approach. In general, standard Bayesian Gibbs sampling and a frequentist approach resulted in similar estimates if heritability of the regarded trait was high. For low heritable traits, the modified Bayesian model, accounting for relatedness between lines in genotype-by-environment interaction, was superior to the standard model.

  11. Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine.

    Directory of Open Access Journals (Sweden)

    Jun Meng

    Full Text Available Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA. Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA-target interactions.Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.

  12. The interaction of affective states and cognitive vulnerabilities in the prediction of non-suicidal self-injury.

    Science.gov (United States)

    Cohen, Jonah N; Stange, Jonathan P; Hamilton, Jessica L; Burke, Taylor A; Jenkins, Abigail; Ong, Mian-Li; Heimberg, Richard G; Abramson, Lyn Y; Alloy, Lauren B

    2015-01-01

    Non-suicidal self-injury (NSSI) is a serious public health concern and remains poorly understood. This study sought to identify both cognitive and affective vulnerabilities to NSSI and examine their interaction in the prediction of NSSI. A series of regressions indicated that low levels of positive affect (PA) moderated the relationships between self-criticism and brooding and NSSI. The associations of self-criticism and brooding with greater frequency of NSSI were attenuated by higher levels of PA. The interaction of cognitive and affective vulnerabilities is discussed within the context of current NSSI theory.

  13. 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

    2017-01-01

    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. PMID:28455415

  14. Developing principles for predicting ionic liquid effects on reaction outcome. The importance of the anion in controlling microscopic interactions.

    Science.gov (United States)

    Keaveney, Sinead T; Haines, Ronald S; Harper, Jason B

    2015-03-28

    A series of ionic liquids containing anions of differing coordination strength were investigated as solvents for the condensation reaction of an alkyl amine and an aromatic aldehyde. As predicted, the rate constant of the process was found to increase with the proportion of the ionic liquid in the reaction mixture. Temperature-dependent kinetic analyses demonstrated that by varying the ability of the anion to interact with the cation the magnitude of both the enthalpy and entropy of activation could be controlled in a predictable manner, with the activation parameters being linearly dependent on the ionic liquid basicity. Interestingly, the unexpected trend in the rate constants observed when altering the anion of the ionic liquid highlighted the presence of more subtle secondary microscopic interactions involving the anion, further emphasizing the fragility of the enthalpy - entropy balance.

  15. Heterogeneous social motives and interactions: the three predictable paths of capability development

    NARCIS (Netherlands)

    Bridoux, F.; Coeurderoy, R.; Durand, R.

    2017-01-01

    Research summary: Limited attention has been paid to the crucial role of individuals' motivation and social interactions in capability development. Building on literature in social psychology and behavioral economics that links heterogeneity in individual social motives to social interactions, we ex

  16. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.

    Science.gov (United States)

    Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Ritchie, Marylyn D

    2015-08-01

    Evaluation of survival models to predict cancer patient prognosis is one of the most important areas of emphasis in cancer research. A binary classification approach has difficulty directly predicting survival due to the characteristics of censored observations and the fact that the predictive power depends on the threshold used to set two classes. In contrast, the traditional Cox regression approach has some drawbacks in the sense that it does not allow for the identification of interactions between genomic features, which could have key roles associated with cancer prognosis. In addition, data integration is regarded as one of the important issues in improving the predictive power of survival models since cancer could be caused by multiple alterations through meta-dimensional genomic data including genome, epigenome, transcriptome, and proteome. Here we have proposed a new integrative framework designed to perform these three functions simultaneously: (1) predicting censored survival data; (2) integrating meta-dimensional omics data; (3) identifying interactions within/between meta-dimensional genomic features associated with survival. In order to predict censored survival time, martingale residuals were calculated as a new continuous outcome and a new fitness function used by the grammatical evolution neural network (GENN) based on mean absolute difference of martingale residuals was implemented. To test the utility of the proposed framework, a simulation study was conducted, followed by an analysis of meta-dimensional omics data including copy number, gene expression, DNA methylation, and protein expression data in breast cancer retrieved from The Cancer Genome Atlas (TCGA). On the basis of the results from breast cancer dataset, we were able to identify interactions not only within a single dimension of genomic data but also between meta-dimensional omics data that are associated with survival. Notably, the predictive power of our best meta-dimensional model

  17. Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions.

    Science.gov (United States)

    Guest, Eleanor J; Aarons, Leon; Houston, J Brian; Rostami-Hodjegan, Amin; Galetin, Aleksandra

    2011-02-01

    Current assessment of drug-drug interaction (DDI) prediction success is based on whether predictions fall within a two-fold range of the observed data. This strategy results in a potential bias toward successful prediction at lower interaction levels [ratio of the area under the concentration-time profile (AUC) in the presence of inhibitor/inducer compared with control is assessment of different DDI prediction algorithms if databases contain large proportion of interactions in this lower range. Therefore, the current study proposes an alternative method to assess prediction success with a variable prediction margin dependent on the particular AUC ratio. The method is applicable for assessment of both induction and inhibition-related algorithms. The inclusion of variability into this predictive measure is also considered using midazolam as a case study. Comparison of the traditional two-fold and the new predictive method was performed on a subset of midazolam DDIs collated from previous databases; in each case, DDIs were predicted using the dynamic model in Simcyp simulator. A 21% reduction in prediction accuracy was evident using the new predictive measure, in particular at the level of no/weak interaction (AUC ratio assessed via the new predictive measure. Thus, the study proposes a more logical method for the assessment of prediction success and its application for induction and inhibition DDIs.

  18. 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.

  19. Cross-comparison of spacecraft-environment interaction model predictions applied to Solar Probe Plus near perihelion

    Energy Technology Data Exchange (ETDEWEB)

    Marchand, R. [Department of Physics, University of Alberta, Edmonton, Alberta T6G 2E1 (Canada); Miyake, Y.; Usui, H. [Graduate School of System Informatics, Kobe University, Kobe 657-8501 (Japan); Deca, J.; Lapenta, G. [Centre for Mathematical Plasma Astrophysics, Mathematics Department, KU Leuven, Celestijnenlaan 200B bus 2400, 3001 Leuven (Belgium); Matéo-Vélez, J. C. [Department of Space Environment, Onera—The French Aerospace Lab, Toulouse (France); Ergun, R. E.; Sturner, A. [Department of Astrophysical and Planetary Science, University of Colorado, Boulder, Colorado 80309 (United States); Génot, V. [Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse, France and CNRS, IRAP, 9 Av. colonel Roche, BP 44346, 31028 Toulouse cedex 4 (France); Hilgers, A. [ESA, ESTEC, Keplerlaan 1, PO Box 299, 2200 AG Noordwijk (Netherlands); Markidis, S. [High Performance Computing and Visualization Department, KTH Royal Institute of Technology, Stockholm (Sweden)

    2014-06-15

    Five spacecraft-plasma models are used to simulate the interaction of a simplified geometry Solar Probe Plus (SPP) satellite with the space environment under representative solar wind conditions near perihelion. By considering similarities and differences between results obtained with different numerical approaches under well defined conditions, the consistency and validity of our models can be assessed. The impact on model predictions of physical effects of importance in the SPP mission is also considered by comparing results obtained with and without these effects. Simulation results are presented and compared with increasing levels of complexity in the physics of interaction between solar environment and the SPP spacecraft. The comparisons focus particularly on spacecraft floating potentials, contributions to the currents collected and emitted by the spacecraft, and on the potential and density spatial profiles near the satellite. The physical effects considered include spacecraft charging, photoelectron and secondary electron emission, and the presence of a background magnetic field. Model predictions obtained with our different computational approaches are found to be in agreement within 2% when the same physical processes are taken into account and treated similarly. The comparisons thus indicate that, with the correct description of important physical effects, our simulation models should have the required skill to predict details of satellite-plasma interaction physics under relevant conditions, with a good level of confidence. Our models concur in predicting a negative floating potential V{sub fl}∼−10V for SPP at perihelion. They also predict a “saturated emission regime” whereby most emitted photo- and secondary electron will be reflected by a potential barrier near the surface, back to the spacecraft where they will be recollected.

  20. GPCR-drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure.

    Science.gov (United States)

    Hu, Jun; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

    2016-02-01

    G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR-drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR-drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR-drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.

  1. Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm.

    Science.gov (United States)

    Jia, J H; Liu, Z; Chen, X; Xiao, X; Liu, B X

    2015-10-02

    Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.

  2. 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.

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

    Indian Academy of Sciences (India)

    Brijesh Kumar Sriwastava; Subhadip Basu; Ujjwal Maulik

    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. 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.

  5. Covariant Spectator Theory of heavy-light and heavy mesons and the predictive power of covariant interaction kernels

    Science.gov (United States)

    Leitão, Sofia; Stadler, Alfred; Peña, M. T.; Biernat, Elmar P.

    2017-01-01

    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.

  6. Covariant Spectator Theory of heavy-light and heavy mesons and the predictive power of covariant interaction kernels

    CERN Document Server

    Leitão, Sofia; Peña, M T; Biernat, Elmar P

    2016-01-01

    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.

  7. 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.

  8. 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, the

  9. Cellulose nanocrystal interactions probed by thin film swelling to predict dispersibility

    Science.gov (United States)

    Reid, Michael S.; Villalobos, Marco; Cranston, Emily D.

    2016-06-01

    The production of well-dispersed reinforced polymer nanocomposites has been limited due to poor understanding of the interactions between components. Measuring the cohesive particle-particle interactions and the adhesive particle-polymer interactions is challenging due to nanoscale dimensions and poor colloidal stability of nanoparticles in many solvents. We demonstrate a new cohesive interaction measurement method using cellulose nanocrystals (CNCs) as a model system; CNCs have recently gained attention in the composites community due to their mechanical strength and renewable nature. Multi-wavelength surface plasmon resonance spectroscopy (SPR) was used to monitor the swelling of CNC thin films to elucidate the primary forces between CNCs. This was achieved by measuring swelling in situ in water, acetone, methanol, acetonitrile, isopropanol, and ethanol and relating the degree of swelling to solvent properties. Films swelled the most in water where we estimate 1.2-1.6 nm spacings between CNCs (or 4-6 molecular layers of water). Furthermore, a correlation was found between film swelling and the solvent's Hildebrand solubility parameter (R2 = 0.9068). The hydrogen bonding component of the solubility parameters was more closely linked to swelling than the polar or dispersive components. The films remained intact in all solvents, and using DLVO theory we have identified van der Waals forces as the main cohesive interaction between CNCs. The trends observed suggest that solvents (and polymers) alone are not sufficient to overcome CNC-CNC cohesion and that external energy is required to break CNC agglomerates. This work not only demonstrates that SPR can be used as a tool to measure cohesive particle-particle interactions but additionally advances our fundamental understanding of CNC interactions which is necessary for the design of cellulose nanocomposites.The production of well-dispersed reinforced polymer nanocomposites has been limited due to poor understanding of

  10. 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.

  11. Two-particle angular correlations in $e^+ e^-$ interactions compared with QCD predictions

    CERN Document Server

    Abreu, P; Adye, T; Adzic, P; Ajinenko, I; Alekseev, G D; Alemany, R; Allport, P P; Almehed, S; Amaldi, Ugo; Amato, S; Anassontzis, E G; Andersson, P; Andreazza, A; Andringa, S; Antilogus, P; Apel, W D; Arnoud, Y; Åsman, B; Augustin, J E; Augustinus, A; Baillon, Paul; Bambade, P; Barão, F; Barbiellini, Guido; Barbier, R; Bardin, Dimitri Yuri; Barker, G; Baroncelli, A; Battaglia, Marco; Baubillier, M; Becks, K H; Begalli, M; Beillière, P; Belokopytov, Yu A; Belous, K S; Benvenuti, Alberto C; Bérat, C; Berggren, M; Bertini, D; Bertrand, D; Besançon, M; Bianchi, F; Bigi, M; Bilenky, S M; Bizouard, M A; Bloch, D; Blom, H M; Bonesini, M; Bonivento, W; Boonekamp, M; Booth, P S L; Borgland, A W; Borisov, G; Bosio, C; Botner, O; Boudinov, E; Bouquet, B; Bourdarios, C; Bowcock, T J V; Boyko, I; Bozovic, I; Bozzo, M; Branchini, P; Brenke, T; Brenner, R A; Brückman, P; Brunet, J M; Bugge, L; Buran, T; Burgsmüller, T; Buschbeck, Brigitte; Buschmann, P; Cabrera, S; Caccia, M; Calvi, M; Camacho-Rozas, A J; Camporesi, T; Canale, V; Carena, F; Carroll, L; Caso, Carlo; Castillo-Gimenez, M V; Cattai, A; Cavallo, F R; Cerruti, C; Chabaud, V; Charpentier, P; Chaussard, L; Checchia, P; Chelkov, G A; Chierici, R; Chliapnikov, P V; Chochula, P; Chorowicz, V; Chudoba, J; Collins, P; Colomer, M; Contri, R; Cortina, E; Cosme, G; Cossutti, F; Cowell, J H; Crawley, H B; Crennell, D J; Crosetti, G; Cuevas-Maestro, J; Czellar, S; Damgaard, G; Davenport, Martyn; Da Silva, W; Deghorain, A; Della Ricca, G; Delpierre, P A; Demaria, N; De Angelis, A; de Boer, Wim; De Brabandere, S; De Clercq, C; De Lotto, B; De Min, A; De Paula, L S; Dijkstra, H; Di Ciaccio, Lucia; Di Diodato, A; Dolbeau, J; Doroba, K; Dracos, M; Drees, J; Dris, M; Duperrin, A; Durand, J D; Ehret, R; Eigen, G; Ekelöf, T J C; Ekspong, Gösta; Ellert, M; Elsing, M; Engel, J P; Erzen, B; Espirito-Santo, M C; Falk, E; Fanourakis, G K; Fassouliotis, D; Fayot, J; Feindt, Michael; Ferrari, P; Ferrer, A; Ferrer-Ribas, E; Fichet, S; Firestone, A; Fischer, P A; Flagmeyer, U; Föth, H; Fokitis, E; Fontanelli, F; Franek, B J; Frodesen, A G; Frühwirth, R; Fulda-Quenzer, F; Fuster, J A; Galloni, A; Gamba, D; Gamblin, S; Gandelman, M; García, C; García, J; Gaspar, C; Gaspar, M; Gasparini, U; Gavillet, P; Gazis, E N; Gelé, D; Gerber, J P; Gerdyukov, L N; Ghodbane, N; Gil, I; Glege, F; Gokieli, R; Golob, B; Gonçalves, P; González-Caballero, I; Gopal, Gian P; Gorn, L; Górski, M; Guz, Yu; Gracco, Valerio; Grahl, J; Graziani, E; Green, C; Gris, P; Grzelak, K; Günther, M; Guy, J; Hahn, F; Hahn, S; Haider, S; Hallgren, A; Hamacher, K; Harris, F J; Hedberg, V; Heising, S; Hernández, J J; Herquet, P; Herr, H; Hessing, T L; Heuser, J M; Higón, E; Holmgren, S O; Holt, P J; Holthuizen, D J; Hoorelbeke, S; Houlden, M A; Hrubec, Josef; Huet, K; Hultqvist, K; Jackson, J N; Jacobsson, R; Jalocha, P; Janik, R; Jarlskog, C; Jarlskog, G; Jarry, P; Jean-Marie, B; Johansson, E K; Jönsson, P E; Joram, C; Juillot, P; Kapusta, F; Karafasoulis, K; Katsanevas, S; Katsoufis, E C; Keränen, R; Khomenko, B A; Khovanskii, N N; Kiiskinen, A P; King, B J; Kjaer, N J; Klapp, O; Klein, H; Kluit, P M; Knoblauch, D; Kokkinias, P; Koratzinos, M; Kostyukhin, V; Kourkoumelis, C; Kuznetsov, O; Krammer, Manfred; Kreuter, C; Krstic, J; Krumshtein, Z; Kubinec, P; Kucewicz, W; Kurvinen, K L; Lamsa, J; Lane, D W; Langefeld, P; Lapin, V; Laugier, J P; Lauhakangas, R; Leder, Gerhard; Ledroit, F; Lefébure, V; Leinonen, L; Leisos, A; Leitner, R; Lenzen, Georg; Lepeltier, V; Lesiak, T; Lethuillier, M; Libby, J; Liko, D; Lipniacka, A; Lippi, I; Lörstad, B; Loken, J G; Lopes, J H; López, J M; López-Fernandez, R; Loukas, D; Lutz, P; Lyons, L; MacNaughton, J N; Mahon, J R; Maio, A; Malek, A; Malmgren, T G M; Malychev, V; Mandl, F; Marco, J; Marco, R P; Maréchal, B; Margoni, M; Marin, J C; Mariotti, C; Markou, A; Martínez-Rivero, C; Martínez-Vidal, F; Martí i García, S; Mastroyiannopoulos, N; Matorras, F; Matteuzzi, C; Matthiae, Giorgio; Masik, J; Mazzucato, F; Mazzucato, M; McCubbin, M L; McKay, R; McNulty, R; McPherson, G; Meroni, C; Meyer, W T; Myagkov, A; Migliore, E; Mirabito, L; Mitaroff, Winfried A; Mjörnmark, U; Moa, T; Møller, R; Mönig, K; Monge, M R; Moreau, X; Morettini, P; Morton, G A; Müller, U; Münich, K; Mulders, M; Mulet-Marquis, C; Muresan, R; Murray, W J; Muryn, B; Myatt, Gerald; Myklebust, T; Naraghi, F; Navarria, Francesco Luigi; Navas, S; Nawrocki, K; Negri, P; Neufeld, N; Neumeister, N; Nicolaidou, R; Nielsen, B S; Nikolaenko, V; Nikolenko, M; Nomokonov, V P; Normand, Ainsley; Nygren, A; Obraztsov, V F; Olshevskii, A G; Onofre, A; Orava, Risto; Orazi, G; Österberg, K; Ouraou, A; Paganoni, M; Paiano, S; Pain, R; Paiva, R; Palacios, J; Palka, H; Papadopoulou, T D; Papageorgiou, K; Pape, L; Parkes, C; Parodi, F; Parzefall, U; Passeri, A; Passon, O; Pegoraro, M; Peralta, L; Pernicka, Manfred; Perrotta, A; Petridou, C; Petrolini, A; Phillips, H T; Piana, G; Pierre, F; Pimenta, M; Piotto, E; Podobnik, T; Pol, M E; Polok, G; Poropat, P; Pozdnyakov, V; Privitera, P; Pukhaeva, N; Pullia, Antonio; Radojicic, D; Ragazzi, S; Rahmani, H; Rakoczy, D; Rames, J; Ratoff, P N; Read, A L; Rebecchi, P; Redaelli, N G; Reid, D; Reinhardt, R; Renton, P B; Resvanis, L K; Richard, F; Rídky, J; Rinaudo, G; Røhne, O M; Romero, A; Ronchese, P; Rosenberg, E I; Rosinsky, P; Roudeau, Patrick; Rovelli, T; Ruhlmann-Kleider, V; Ruiz, A; Saarikko, H; Sacquin, Yu; Sadovskii, A; Sajot, G; Salt, J; Sampsonidis, D; Sannino, M; Schneider, H; Schwemling, P; Schwickerath, U; Schyns, M A E; Scuri, F; Seager, P; Sedykh, Yu; Segar, A M; Sekulin, R L; Shellard, R C; Sheridan, A; Siebel, M; Silvestre, R; Simard, L C; Simonetto, F; Sissakian, A N; Skaali, T B; Smadja, G; Smirnova, O G; Smith, G R; Sopczak, André; Sosnowski, R; Spassoff, Tz; Spiriti, E; Sponholz, P; Squarcia, S; Stampfer, D; Stanescu, C; Stanic, S; Stapnes, Steinar; Stevenson, K; Stocchi, A; Strauss, J; Strub, R; Stugu, B; Szczekowski, M; Szeptycka, M; Tabarelli de Fatis, T; Chikilev, O G; Tegenfeldt, F; Terranova, F; Thomas, J; Tilquin, A; Timmermans, J; Tkatchev, L G; Todorov, T; Todorova, S; Toet, D Z; Tomaradze, A G; Tomé, B; Tonazzo, A; Tortora, L; Tranströmer, G; Treille, D; Tristram, G; Troncon, C; Tsirou, A L; Turluer, M L; Tyapkin, I A; Tzamarias, S; Überschär, B; Ullaland, O; Uvarov, V; Valenti, G; Vallazza, E; van Apeldoorn, G W; van Dam, P; Van Eldik, J; Van Lysebetten, A; Van Vulpen, I B; Vassilopoulos, N; Vegni, G; Ventura, L; Venus, W A; Verbeure, F; Verlato, M; Vertogradov, L S; Verzi, V; Vilanova, D; Vitale, L; Vlasov, E; Vodopyanov, A S; Voulgaris, G; Vrba, V; Wahlen, H; Walck, C; Weiser, C; Wicke, D; Wickens, J H; Wilkinson, G R; Winter, M; Witek, M; Wolf, G; Yi, J; Yushchenko, O P; Zalewska-Bak, A; Zalewski, Piotr; Zavrtanik, D; Zevgolatakos, E; Zimin, N I; Zucchelli, G C; Zumerle, G

    1998-01-01

    Two--particle angular correlations in jet cones have been measured in $e^+e^-$ annihilation into hadrons at LEP energies ($\\sqrt{s}=$ 91 and 183~GeV) and are compared with QCD predictions using the LPHD hypothesis. Two different functions have been tested. While the differentially normalized correlation function shows substantial deviations from the predictions, a globally normalized correlation function agrees well. The size of $\\alpha_S^{\\rm eff}$ (and other QCD parameters) and its running with the relevant angular scale, the validity of LPHD, and problems due to non--perturbative effects are discussed critically.

  12. Morphology predicts species' functional roles and their degree of specialization in plant–frugivore interactions

    Science.gov (United States)

    Dehling, D. Matthias; Schaefer, H. Martin; Böhning-Gaese, Katrin; Schleuning, Matthias

    2016-01-01

    Species' functional roles in key ecosystem processes such as predation, pollination or seed dispersal are determined by the resource use of consumer species. An interaction between resource and consumer species usually requires trait matching (e.g. a congruence in the morphologies of interaction partners). Species' morphology should therefore determine species' functional roles in ecological processes mediated by mutualistic or antagonistic interactions. We tested this assumption for Neotropical plant–bird mutualisms. We used a new analytical framework that assesses a species's functional role based on the analysis of the traits of its interaction partners in a multidimensional trait space. We employed this framework to test (i) whether there is correspondence between the morphology of bird species and their functional roles and (ii) whether morphologically specialized birds fulfil specialized functional roles. We found that morphological differences between bird species reflected their functional differences: (i) bird species with different morphologies foraged on distinct sets of plant species and (ii) morphologically distinct bird species fulfilled specialized functional roles. These findings encourage further assessments of species' functional roles through the analysis of their interaction partners, and the proposed analytical framework facilitates a wide range of novel analyses for network and community ecology. PMID:26817779

  13. Interactions between donor Agreeableness and recipient characteristics in predicting charitable donation and positive social evaluation

    Directory of Open Access Journals (Sweden)

    Tal Yarkoni

    2015-08-01

    Full Text Available Agreeable people are more likely to display prosocial attitudes and helpful behavior in a broad range of situations. Here we show that this tendency interacts with the personal characteristics of interaction partners. In an online study (n = 284, participants were given the opportunity to report attitudes toward and make monetary donations to needy individuals who were described in dynamically generated biographies. Using a machine learning and multilevel modeling framework, we tested three potential explanations for the facilitatory influence of Agreeableness on charitable behavior. We find that Agreeableness preferentially increased donations and prosocial attitudes toward targets normatively rated as being more deserving. Our results advance understanding of person-by-situation interactions in the context of charitable behavior and prosocial attitudes.

  14. Interactions between donor Agreeableness and recipient characteristics in predicting charitable donation and positive social evaluation.

    Science.gov (United States)

    Yarkoni, Tal; Ashar, Yoni K; Wager, Tor D

    2015-01-01

    Agreeable people are more likely to display prosocial attitudes and helpful behavior in a broad range of situations. Here we show that this tendency interacts with the personal characteristics of interaction partners. In an online study (n = 284), participants were given the opportunity to report attitudes toward and make monetary donations to needy individuals who were described in dynamically generated biographies. Using a machine learning and multilevel modeling framework, we tested three potential explanations for the facilitatory influence of Agreeableness on charitable behavior. We find that Agreeableness preferentially increased donations and prosocial attitudes toward targets normatively rated as being more deserving. Our results advance understanding of person-by-situation interactions in the context of charitable behavior and prosocial attitudes.

  15. Predicting the CRIP1a-cannabinoid 1 receptor interactions with integrated molecular modeling approaches

    Science.gov (United States)

    Ahmed, Mostafa H.; Kellogg, Glen E.; Selley, Dana E.; Safo, Martin; Zhang, Yan

    2015-01-01

    Cannabinoid receptors are a family of G-protein coupled receptors that are involved in a wide variety of physiological processes and diseases. One of the key regulators that are unique to cannabinoid receptors is the cannabinoid receptor interacting proteins (CRIPs). Among them CRIP1a was found to decrease the constitutive activity of the cannabinoid type-1 receptor (CB1R). The aim of this study is to gain an understanding of the interaction between CRIP1a and CB1R through using different computational techniques. The generated model demonstrated several key putative interactions between CRIP1a and CB1R, including those involving Lys130 of CRIP1a. PMID:24461351

  16. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes.

    Science.gov (United States)

    Teng, W J; Zhou, C; Liu, L J; Cao, X J; Zhuang, J; Liu, G X; Sun, C G

    2016-05-23

    Wilms' tumor (WT), or nephroblastoma, is the most common malignant renal cancer that affects the pediatric population. Great progress has been achieved in the treatment of WT, but it cannot be cured at present. Nonetheless, a protein-protein interaction network of WT should provide some new ideas and methods. The purpose of this study was to analyze the protein-protein interaction network of WT. We screened the confirmed disease-related genes using the Online Mendelian Inheritance in Man database, created a protein-protein interaction network based on biological function in the Cytoscape software, and detected molecular complexes and relevant pathways that may be included in the network. The results showed that the protein-protein interaction network of WT contains 654 nodes, 1544 edges, and 5 molecular complexes. Among them, complex 1 is predicted to be related to the Jak-STAT signaling pathway, regulation of hematopoiesis by cytokines, cytokine-cytokine receptor interaction, cytokine and inflammatory responses, and hematopoietic cell lineage pathways. Molecular complex 4 shows a correlation of WT with colorectal cancer and the ErbB signaling pathway. The proposed method can provide the bioinformatic foundation for further elucidation of the mechanisms of WT development.

  17. The Attachment Doll Play Assessment: Predictive Validity with Concurrent Mother-Child Interaction and Maternal Caregiving Representations

    Science.gov (United States)

    George, Carol; Solomon, Judith

    2016-01-01

    Attachment is central to the development of children’s regulatory processes. It has been associated with developmental and psychiatric health across the life span, especially emotional and behavioral regulation of negative affect when stressed (Schore, 2001; Schore and Schore, 2008). Assessment of attachment patterns provides a critical frame for understanding emerging developmental competencies and formulating treatment and intervention. Play-based attachment assessments provide access to representational models of attachment, which are regarded in attachment theory as the central organizing mechanisms associated with stability or change (Bowlby, 1969/1982; Bretherton and Munholland, 2008). The Attachment Doll Play Assessment (ADPA, George and Solomon, 1990–2016; Solomon et al., 1995) is a prominent established representational attachment measure for children aged early latency through childhood. This study examines the predictive validity of the ADPA to caregiving accessibility and responsiveness assessed from mother-child interaction and maternal representation. Sixty nine mothers and their 5–7-year-old children participated in this study. Mother-child interaction was observed during a pre-separation dyadic interaction task. Caregiving representations were rated from the Caregiving Interview (George and Solomon, 1988/1993/2005/2007). Child security with mother was associated with positive dyadic interaction and flexibly integrated maternal caregiving representations. Child controlling/disorganized attachments were significantly associated with problematic dyadic interaction and dysregulated-helpless maternal caregiving representations. The clinical implications and the use of the ADPA in clinical and educational settings are discussed. PMID:27803683

  18. The Attachment Doll Play Assessment: Predictive Validity with Concurrent Mother-Child Interaction and Maternal Caregiving Representations

    Directory of Open Access Journals (Sweden)

    Carol George

    2016-10-01

    Full Text Available Attachment is central to the development of children’s regulatory processes. It has been associated with developmental and psychiatric health across the life span, especially emotional and behavioral regulation of negative affect when stressed (Schore, 2001; Schore & Schore, 2008. Assessment of attachment patterns provides a critical frame for understanding emerging developmental competencies and formulating treatment and intervention. Play-based attachment assessments provide access to representational models of attachment, which are regarded in attachment theory as the central organizing mechanisms associated with stability or change (Bowlby, 1969/1982; Bretherton & Munholland, 2008. The Attachment Doll Play Assessment (ADPA, George & Solomon, 1990-2016; Solomon, George, & De Jong, 1995 is a prominent established representational attachment measure for children aged early latency through childhood. This study examines the predictive validity of the ADPA to caregiving accessibility and responsiveness assessed from mother-child interaction and maternal representation. Sixty nine mothers and their 5-7-year-old children participated in this study. Mother-child interaction was observed during a pre-separation dyadic interaction task. Caregiving representations were rated from the Caregiving Interview (George & Solomon, 1988/1993/2005/2007. Child security with mother was associated with positive dyadic interaction and flexibly integrated maternal caregiving representations. Child controlling/disorganized attachments were significantly associated with problematic dyadic interaction and dysregulated-helpless maternal caregiving representations. The clinical implications and the use of the ADPA in clinical and educational settings are discussed.

  19. Predicting important residues and interaction pathways in proteins using Gaussian Network Model: binding and stability of HLA proteins.

    Directory of Open Access Journals (Sweden)

    Turkan Haliloglu

    Full Text Available A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed.

  20. Classroom Dimensions Predict Early Peer Interaction when Children Are Diverse in Ethnicity, Race, and Home Language

    Science.gov (United States)

    Howes, Carollee; Guerra, Alison Wishard; Fuligni, Allison; Zucker, Eleanor; Lee, Linda; Obregon, Nora B.; Spivak, Asha

    2011-01-01

    The purpose of this study was to test a model for predicting preschool-age children's behaviors with peers from dimensions of the classroom and teacher-child relationship quality when the children were from diverse race, ethnic, and home language backgrounds. Eight hundred children, (M=age 63 months, SD=8.1 months), part of the National Evaluation…

  1. How Minimal Grade Goals and Self-Control Capacity Interact in Predicting Test Grades

    Science.gov (United States)

    Bertrams, Alex

    2012-01-01

    The present research examined the prediction of school students' grades in an upcoming math test via their minimal grade goals (i.e., the minimum grade in an upcoming test one would be satisfied with). Due to its significance for initiating and maintaining goal-directed behavior, self-control capacity was expected to moderate the relation between…

  2. 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.

  3. Prediction of barley progeny performance in the presence of genotype-environment interaction

    NARCIS (Netherlands)

    Schut, J.W.; Dourleijn, C.J.

    2000-01-01

    Twenty recombinant inbred line (RIL) populations of European two-row spring barley and their parents were tested in six environments in the Netherlands to investigate the prediction of progeny yield level, yield variance, stability level and stability variance, based on parent information. Progeny y

  4. Classroom Dimensions Predict Early Peer Interaction when Children Are Diverse in Ethnicity, Race, and Home Language

    Science.gov (United States)

    Howes, Carollee; Guerra, Alison Wishard; Fuligni, Allison; Zucker, Eleanor; Lee, Linda; Obregon, Nora B.; Spivak, Asha

    2011-01-01

    The purpose of this study was to test a model for predicting preschool-age children's behaviors with peers from dimensions of the classroom and teacher-child relationship quality when the children were from diverse race, ethnic, and home language backgrounds. Eight hundred children, (M=age 63 months, SD=8.1 months), part of the National Evaluation…

  5. A multi-scale continuum model of skeletal muscle mechanics predicting force enhancement based on actin-titin interaction.

    Science.gov (United States)

    Heidlauf, Thomas; Klotz, Thomas; Rode, Christian; Altan, Ekin; Bleiler, Christian; Siebert, Tobias; Röhrle, Oliver

    2016-12-01

    Although recent research emphasises the possible role of titin in skeletal muscle force enhancement, this property is commonly ignored in current computational models. This work presents the first biophysically based continuum-mechanical model of skeletal muscle that considers, in addition to actin-myosin interactions, force enhancement based on actin-titin interactions. During activation, titin attaches to actin filaments, which results in a significant reduction in titin's free molecular spring length and therefore results in increased titin forces during a subsequent stretch. The mechanical behaviour of titin is included on the microscopic half-sarcomere level of a multi-scale chemo-electro-mechanical muscle model, which is based on the classic sliding-filament and cross-bridge theories. In addition to titin stress contributions in the muscle fibre direction, the continuum-mechanical constitutive relation accounts for geometrically motivated, titin-induced stresses acting in the muscle's cross-fibre directions. Representative simulations of active stretches under maximal and submaximal activation levels predict realistic magnitudes of force enhancement in fibre direction. For example, stretching the model by 20 % from optimal length increased the isometric force at the target length by about 30 %. Predicted titin-induced stresses in the muscle's cross-fibre directions are rather insignificant. Including the presented development in future continuum-mechanical models of muscle function in dynamic situations will lead to more accurate model predictions during and after lengthening contractions.

  6. 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).

  7. 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.

  8. In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond.

    Science.gov (United States)

    Ai, Ni; Fan, Xiaohui; Ekins, Sean

    2015-06-23

    Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

  9. Research on drug-receptor interactions and prediction of drug activity via oriented immobilized receptor capillary electrophoresis.

    Science.gov (United States)

    Liu, Chunye; Zhang, Xuejiao; Jing, Hui; Miao, Yanqing; Zhao, Lingzhi; Han, Yan; Cui, Cuixia

    2015-10-01

    Oriented covalent immobilized β2 -adrenergic receptor (β2 -AR) CE (OIRCE) was developed to determine the interactions between a set of natural extracts of Radix Paeoniae Rubra (NERPR) and β2 -AR, and to predict the activity of NERPR. The inner capillary surface is chemically bonded with stable β2 -AR coating via microwave-assisted technical synthesis. The modified capillaries were characterized via infrared spectroscopy and fluorescence microscopy. Furthermore, the bonding amounts of β2 -AR were first obtained via fluorescence spectroscopy method. In determining the amount of bonded β2 -AR, the regression equation A  =  576 707C + 35.449 and the correlation coefficient 0.9995 were obtained. This result revealed an excellent linear relationship in the range of 2 × 10(-4)  mg/mL to 1 × 10(-3)  mg/mL. The normalized capacity factor (KRCE ) was obtained using OIRCE in evaluating drug-receptor interactions. Related theories and equations were used to calculate KRCE values from apparent migration times of a solute and EOF. The order of KRCE and the binding constant (Kb ) values between drugs and β2 -AR was well consistent. The results confirmed that the OIRCE and KRCE values can be effectually used to investigate drug-receptor interactions, and OIRCE has the potential to predict drug activity and to select leading compounds from natural chemicals.

  10. Prejudiced interactions: implicit racial bias reduces predictive simulation during joint action with an out-group avatar.

    Science.gov (United States)

    Sacheli, Lucia Maria; Christensen, Andrea; Giese, Martin A; Taubert, Nick; Pavone, Enea Francesco; Aglioti, Salvatore Maria; Candidi, Matteo

    2015-02-17

    During social interactions people automatically apply stereotypes in order to rapidly categorize others. Racial differences are among the most powerful cues that drive these categorizations and modulate our emotional and cognitive reactivity to others. We investigated whether implicit racial bias may also shape hand kinematics during the execution of realistic joint actions with virtual in- and out-group partners. Caucasian participants were required to perform synchronous imitative or complementary reach-to-grasp movements with avatars that had different skin color (white and black) but showed identical action kinematics. Results demonstrate that stronger visuo-motor interference (indexed here as hand kinematics differences between complementary and imitative actions) emerged: i) when participants were required to predict the partner's action goal in order to on-line adapt their own movements accordingly; ii) during interactions with the in-group partner, indicating the partner's racial membership modulates interactive behaviors. Importantly, the in-group/out-group effect positively correlated with the implicit racial bias of each participant. Thus visuo-motor interference during joint action, likely reflecting predictive embodied simulation of the partner's movements, is affected by cultural inter-individual differences.

  11. 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...

  12. 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…

  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. Illegitimacy and identity threat in (inter)action : Predicting intergroup orientations among minority group members

    NARCIS (Netherlands)

    Livingstone, Andrew G.; Spears, Russell; Manstead, Antony S. R.; Bruder, Martin

    2009-01-01

    We test the hypothesis that intergroup orientations among minority group members are shaped by the interaction between the perceived illegitimacy of intergroup relations and identity threat appraisals, as well as their main effects. This is because together they serve to focus emotion-mediated react

  15. Mercury and psychosocial stress exposure interact to predict maternal diurnal cortisol during pregnancy.

    Science.gov (United States)

    Schreier, Hannah M C; Hsu, Hsiao-Hsien; Amarasiriwardena, Chitra; Coull, Brent A; Schnaas, Lourdes; Téllez-Rojo, Martha María; Tamayo y Ortiz, Marcela; Wright, Rosalind J; Wright, Robert O

    2015-03-27

    Disrupted maternal prenatal cortisol production influences offspring development. Factors influencing the hypothalamic-pituitary-adrenal axis include social (e.g., stressful life events) and physical/chemical (e.g., toxic metals) pollutants. Mercury (Hg) is a common contaminant of fish and exposure is widespread in the US. No prior study has examined the joint associations of stress and mercury with maternal cortisol profiles in pregnancy. To investigate potential synergistic influences of prenatal stress and Hg exposures on diurnal cortisol in pregnant women. Analyses included 732 women (aged 27.4 ± 5.6 years) from a Mexico City pregnancy cohort. Participants collected saliva samples on two consecutive days (mean 19.52 ± 3.00 weeks gestation) and reported life stressors over the past 6 months. Hg was assessed in toe nail clippings collected during pregnancy. There were no main effects of Hg or psychosocial stress exposure on diurnal cortisol (ps > .20) but strong evidence of interaction effects on cortisol slope (interaction B = .006, SE = .003, p = .034) and cortisol at times 1 and 2 (interaction B = -.071, SE = .028, p = .013; B = -.078, SE = .032, p = .014). Women above the median for Hg and psychosocial stress exposure experienced a blunted morning cortisol response compared to women exposed to higher stress but lower Hg levels. Social and physical environmental factors interact to alter aspects of maternal diurnal cortisol during pregnancy. Research focusing solely on either domain may miss synergistic influences with potentially important consequences to the offspring.

  16. Reflection in thought and action: Maternal parenting reflectivity predicts mind-minded comments and interactive behavior.

    Science.gov (United States)

    Rosenblum, Katherine L; McDonough, Susan C; Sameroff, Arnold J; Muzik, Maria

    2008-07-01

    Recent research has identified mothers' mental reflective functioning and verbal mind-minded comments as important predictors of subsequent infant attachment security. In the present study, we examine associations between mothers' (N = 95) parenting reflectivity expressed in an interview and observed parenting behavior, including verbal mind-minded comments and interactive behavior during interaction with their 7-month-old infants. Parenting reflectivity was coded from the Working Model of the Child Interview. Maternal behavior was assessed via observations of mother-infant interaction during free play and structured teaching tasks. Both maternal appropriate mind-minded comments as well as other indicators of maternal interactive behavior were coded. Parenting reflectivity was positively correlated with mind-minded comments and behavioral sensitivity. Hierarchical multiple regression analyses indicated that parenting reflectivity contributed to maternal behavior beyond the contributions of mothers' educational status and depression symptoms. Discussion emphasizes the importance of individual differences in parental capacity to accurately perceive and mentalize their infants' experience, and the consequences of these differences for caregiving behavior. Copyright © 2008 Michigan Association for Infant Mental Health.

  17. 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…

  18. Predicting of regional transpiration at elevated atmospheric CO2: influence of the PBL vegetation interaction.

    NARCIS (Netherlands)

    Jacobs, C.M.J.; Bruin, de H.A.R.

    1997-01-01

    A coupled planetary boundary layer (PBL)-vegetation model is used to study the influence of the PBL-vegetation interaction and the ambient CO2 concentration on surface resistance rs and regional transpiration E. Vegetation is described using the big-leaf model in which rs is modeled by means of a

  19. Predicting of regional transpiration at elevated atmospheric CO2: influence of the PBL vegetation interaction.

    NARCIS (Netherlands)

    Jacobs, C.M.J.; Bruin, de H.A.R.

    1997-01-01

    A coupled planetary boundary layer (PBL)-vegetation model is used to study the influence of the PBL-vegetation interaction and the ambient CO2 concentration on surface resistance rs and regional transpiration E. Vegetation is described using the big-leaf model in which rs is modeled by means of a co

  20. 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…

  1. Do the Naive Know Best? The Predictive Power of Naive Ratings of Couple Interactions

    Science.gov (United States)

    Baucom, Katherine J. W.; Baucom, Brian R.; Christensen, Andrew

    2012-01-01

    We examined the utility of naive ratings of communication patterns and relationship quality in a large sample of distressed couples. Untrained raters assessed 10-min videotaped interactions from 134 distressed couples who participated in both problem-solving and social support discussions at each of 3 time points (pre-therapy, post-therapy, and…

  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. Interactions in protein mixtures. Part II: A virial approach to predict phase behavior

    NARCIS (Netherlands)

    Ersch, C.; Linden, E. van der; Martin, A.; Venema, P.

    2016-01-01

    The interaction of proteins (b-lactoglobulin, Bovine Serum Albumin (BSA), gelatins and whey protein isolate (WPI)) in solution was quantified by measuring their second virial coefficient using membrane osmometry. At neutral pH below 20e40 mM ionic strength, electrostatic repulsion dominated the

  4. 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...

  5. Interactions in protein mixtures. Part II: A virial approach to predict phase behavior

    NARCIS (Netherlands)

    Ersch, C.; Linden, E. van der; Martin, A.; Venema, P.

    2016-01-01

    The interaction of proteins (b-lactoglobulin, Bovine Serum Albumin (BSA), gelatins and whey protein isolate (WPI)) in solution was quantified by measuring their second virial coefficient using membrane osmometry. At neutral pH below 20e40 mM ionic strength, electrostatic repulsion dominated the inte

  6. Predicting Acceptance of Mobile Technology for Aiding Student-Lecturer Interactions: An Empirical Study

    Science.gov (United States)

    Gan, Chin Lay; Balakrishnan, Vimala

    2017-01-01

    The current study sets out to identify determinants affecting tertiary students' behavioural intentions to use mobile technology in lectures. The study emphasises that the reason for using mobile technology in classrooms with large numbers of students is to facilitate interactions among students and lecturers. The proposed conceptual framework has…

  7. 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

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

    Science.gov (United States)

    Buffone, Anneke E K; Poulin, Michael J

    2014-11-01

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

  9. An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

    Science.gov (United States)

    Zaitchik, B. F.; Berhane, F.; Tadesse, T.

    2015-12-01

    We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

  10. Corneosurfametry: a predictive assessment of the interaction of personal-care cleansing products with human stratum corneum.

    Science.gov (United States)

    Piérard, G E; Goffin, V; Piérard-Franchimont, C

    1994-01-01

    Corneosurfametry is introduced as a noninvasive quantitative test rating the interaction between surfactants and human stratum corneum. It may be used as a predictive irritancy test. Surfactants present in personal-care products elicit multiple effects on the stratum corneum. With upcoming regulations avoiding animal experiments and ethical considerations for human testing, there is a need for new in vitro methods evaluating irritancy. Corneosurfametry entails collection of cyanoacrylate skin surface strippings and short contact time with surfactants followed by staining samples with toluidine blue and basic fuchsin dyes. Measurements are made by reading the color of samples using reflectance colorimetry. The intensity of color increases with irritancy potential of the surfactant. Results are reproducible, and great differences are noted among a series of diluted shampoos, shower gels and facial cleansing gels. Corneosurfametry is proposed as a rapid in vitro method allowing a predictive grading of surfactants related to irritancy.

  11. Seismic Proofing Capability of the Accumulated Semiactive Hydraulic Damper as an Active Interaction Control Device with Predictive Control

    Directory of Open Access Journals (Sweden)

    Ming-Hsiang Shih

    2016-01-01

    Full Text Available The intensity of natural disasters has increased recently, causing buildings’ damages which need to be reinforced to prevent their destruction. To improve the seismic proofing capability of Accumulated Semiactive Hydraulic Damper, it is converted to an Active Interaction Control device and synchronous control and predictive control methods are proposed. The full-scale shaking table test is used to test and verify the seismic proofing capability of the proposed AIC with these control methods. This study examines the shock absorption of test structure under excitation by external forces, influences of prediction time, stiffness of the auxiliary structure, synchronous switching, and asynchronous switching on the control effects, and the influence of control locations of test structure on the control effects of the proposed AIC. Test results show that, for the proposed AIC with synchronous control and predictive control of 0.10~0.13 seconds, the displacement reduction ratios are greater than 71%, the average acceleration reduction ratios are, respectively, 36.2% and 36.9%, at the 1st and 2nd floors, and the average base shear reduction ratio is 29.6%. The proposed AIC with suitable stiffeners for the auxiliary structure at each floor with synchronous control and predictive control provide high reliability and practicability for seismic proofing of buildings.

  12. 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.

  13. 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.

  14. Mathematical simulation of interactions of protein molecules and prediction of their reactivity

    Science.gov (United States)

    Kulikov, K. G.; Koshlan, T. V.

    2016-10-01

    A physical model of interactions of protein molecules has been developed. The regularities of their reactivity have been studied using electrostatics methods for two histone dimers H2A-H2B and H3-H4 assembled from monomers. The formation of histone dimers from different monomers has been simulated and their ability to the formation of stable compounds has been investigated by analyzing the potential energy matrix using the condition number. The results of a simulation of the electrostatic interaction in the formation of dimers from complete amino acid sequences of selected proteins and their truncated analogs have been considered. The calculations have been performed taking into account the screening of the electrostatic charge of charged amino acids for different concentrations of the monovalent salt using the Gouy-Chapman theory.

  15. Peptide Suboptimal Conformation Sampling for the Prediction of Protein-Peptide Interactions.

    Science.gov (United States)

    Lamiable, Alexis; Thévenet, Pierre; Eustache, Stephanie; Saladin, Adrien; Moroy, Gautier; Tuffery, Pierre

    2017-01-01

    The blind identification of candidate patches of interaction on the protein surface is a difficult task that can hardly be accomplished without a heuristic or the use of simplified representations to speed up the search. The PEP-SiteFinder protocol performs a systematic blind search on the protein surface using a rigid docking procedure applied to a limited set of peptide suboptimal conformations expected to approximate satisfactorily the conformation of the peptide in interaction. All steps rely on a coarse-grained representation of the protein and the peptide. While simple, such a protocol can help to infer useful information, assuming a critical analysis of the results. Moreover, such a protocol can be extended to a semi-flexible protocol where the suboptimal conformations are directly folded in the vicinity of the receptor.

  16. Enabling Computational Technologies for the Accurate Prediction/Description of Molecular Interactions in Condensed Phases

    Science.gov (United States)

    2014-10-08

    models to compute accurately the molecular interactions between a mobile or stationary phase and a target substrate or analyte , which are fundamental...mobile or stationary phase and a target substrate or analyte , which are fundamental to diverse technologies, e.g., sensor or separation design. With...D. G., New Orleans, LA, April 9, 2013. 223rd Electrochemical Society Meeting, Continuum Solvation Models for Computational Electrochemistry

  17. Wave Current Interactions and Wave-blocking Predictions Using NHWAVE Model

    Science.gov (United States)

    2013-03-01

    release; distribution is unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) Wave blocking in river inlets is examined using the...14. SUBJECT TERMS wave blocking, wave-current interactions, SWASH, NHWAVE 15. NUMBER OF PAGES 61 16. PRICE CODE 17. SECURITY CLASSIFICATION...Renold’s Averaged Navier-Stokes Equations VOF Volume of Fluid MAC Marker and Cell SPH Smoothed Partical Hydrodynamics SWASH Simulating Waves

  18. 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.

  19. How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction.

    Science.gov (United States)

    Ahmadi, Ahmadreza; Tani, Jun

    2017-08-01

    The current paper examines how a recurrent neural network (RNN) model using a dynamic predictive coding scheme can cope with fluctuations in temporal patterns through generalization in learning. The conjecture driving this present inquiry is that a RNN model with multiple timescales (MTRNN) learns by extracting patterns of change from observed temporal patterns, developing an internal dynamic structure such that variance in initial internal states account for modulations in corresponding observed patterns. We trained a MTRNN with low-dimensional temporal patterns, and assessed performance on an imitation task employing these patterns. Analysis reveals that imitating fluctuated patterns consists in inferring optimal internal states by error regression. The model was then tested through humanoid robotic experiments requiring imitative interaction with human subjects. Results show that spontaneous and lively interaction can be achieved as the model successfully copes with fluctuations naturally occurring in human movement patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. An analytical method of predicting Lee-Kesler-Ploecker binary interaction coefficients: Part 1, For non-polar hydrocarbon mixtures

    Energy Technology Data Exchange (ETDEWEB)

    Sand, J.R.

    1994-12-31

    An analytical method is proposed for finding numerical values of binary interaction coefficients for non-polar hydrocarbon mixtures when the Lee-Kesler (LK) equation of state is applied. The method is based on solving simultaneous equations, which are Ploecker`s mixing rules for pseudocritical parameters of a mixture, and the Lee-Kesler equation for the saturation line. For a hydrocarbon mixture, the method allows prediction of {kappa}{sub ij} interaction coefficients (ICs) which are close to values obtained by processing experimental p-v-t data on the saturation line and subsequent averaging. For mixtures of hydrocarbon molecules containing from 2 to 9 carbon atoms, the divergence between calculated and experimentally based ICs is no more than {plus_minus}0.4%. The possibility of extending application of this method to other non-polar substances is discussed.

  1. 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

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

    OpenAIRE

    2015-01-01

    Children’s observed effortful control (EC) at 30, 42, and 54 months (n = 145) was predicted from the interaction between mothers’ observed parenting with their 30-month-olds and three variants of the solute carrier family C6, member 3 (SLC6A3) dopamine transporter gene (single nucleotide polymorphisms in intron8 and intron13, and a 40 base pair variable number tandem repeat [VNTR] in the 3′-untranslated region [UTR]), as well as haplotypes of these variants. Significant moderating effects wer...

  3. 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.

  4. Dynamic vortex interactions with flexible fibers and edges for prediction of owl noise suppression

    Science.gov (United States)

    Korykora, Sarah; Jaworski, Justin

    2015-11-01

    The compliant trailing-edge fringe of owls and the soft downy material on their upper wing surfaces are thought to enable their silent flight by weakening the interaction of boundary layer turbulence with these flexible structures. Previous analysis of turbulence noise generation by wave-bearing elastic edges have shown that the far-field acoustic power scaling can be weakened by up to the square of the Mach number relative to a rigid edge. However, it is unclear whether or not the wave-bearing feature or simply the flexible nature of the edge scatterer produces this noise suppression. To assess this distinction, a dynamic vortex interaction model is developed whereby the motion of a line vortex round a rigid but elastically-restrained wall-mounted fiber or trailing edge is determined numerically. Special attention is paid to the dynamic interaction between the flexible structure and vortex, which is accomplished via a conformal mapping relationship determined in closed form. Results from this analysis seek to develop a vortex sound model to discern the effect of flexible versus wave-bearing scatterers on turbulence noise suppression and help explain the mechanisms of silent owl flight.

  5. For better and for worse: genes and parenting interact to predict future behavior in romantic relationships.

    Science.gov (United States)

    Masarik, April S; Conger, Rand D; Donnellan, M Brent; Stallings, Michael C; Martin, Monica J; Schofield, Thomas J; Neppl, Tricia K; Scaramella, Laura V; Smolen, Andrew; Widaman, Keith F

    2014-06-01

    We tested the differential susceptibility hypothesis with respect to connections between interactions in the family of origin and subsequent behaviors with romantic partners. Focal or target participants (G2) in an ongoing longitudinal study (N = 352) were observed interacting with their parents (G1) during adolescence and again with their romantic partners in adulthood. Independent observers rated positive engagement and hostility by G1 and G2 during structured interaction tasks. We created an index for hypothesized genetic plasticity by summing G2's allelic variation for polymorphisms in 5 genes (serotonin transporter gene [linked polymorphism], 5-HTT; ankyrin repeat and kinase domain containing 1 gene/dopamine receptor D2 gene, ANKK1/DRD2; dopamine receptor D4 gene, DRD4; dopamine active transporter gene, DAT; and catechol-O-methyltransferase gene, COMT). Consistent with the differential susceptibility hypothesis, G2s exposed to more hostile and positively engaged parenting behaviors during adolescence were more hostile or positively engaged toward a romantic partner if they had higher scores on the genetic plasticity index. In short, genetic factors moderated the connection between earlier experiences in the family of origin and future romantic relationship behaviors, for better and for worse.

  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. Shock—Boundary Layer Interaction Control,Predictions Using a Viscous—Inviscid Interaction Procedure and a Navier—Stokes Solver

    Institute of Scientific and Technical Information of China (English)

    G.Simandirakis; B.Bouras; 等

    1997-01-01

    The present contribution describes two prediction methods for flows around transonic airfoils,including shock control devices.The whole work was done in the frame of the European Shock Control Investigation Project EUROSHOCK-AER-2,and the global objective was the improvement of the flight performance,in transonic speed,in terms of cruise speed,fuel consumption and exhaust emissions for both laminar and turbulent wings,More specifically the Passive control of shock/boundary layer interaction,whereby pary of the solid surface of the airfoil is replaced by a porous surface over a shallow cavity,has been shown to be a means of improving the aerodynamic characteristics of supercritical airfoils.

  8. 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

  9. Interaction between the MTHFR C677T polymorphism and traumatic childhood events predicts depression.

    Science.gov (United States)

    Lok, A; Bockting, C L H; Koeter, M W J; Snieder, H; Assies, J; Mocking, R J T; Vinkers, C H; Kahn, R S; Boks, M P; Schene, A H

    2013-07-30

    Childhood trauma is associated with the onset and recurrence of major depressive disorder (MDD). The thermolabile T variant of the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism (rs1801133) is associated with a limited (oxidative) stress defense. Therefore, C677T MTHFR could be a potential predictor for depressive symptomatology and MDD recurrence in the context of traumatic stress during early life. We investigated the interaction between the C677T MTHFR variant and exposure to traumatic childhood events (TCEs) on MDD recurrence during a 5.5-year follow-up in a discovery sample of 124 patients with recurrent MDD and, in an independent replication sample, on depressive symptomatology in 665 healthy individuals from the general population. In the discovery sample, Cox regression analysis revealed a significant interaction between MTHFR genotype and TCEs on MDD recurrence (P=0.017). Over the 5.5-year follow-up period, median time to recurrence was 191 days for T-allele carrying patients who experienced TCEs (T+ and TCE+); 461 days for T- and TCE+ patients; 773 days for T+ and TCE- patients and 866 days for T- and TCE- patients. In the replication sample, a significant interaction was present between the MTHFR genotype and TCEs on depressive symptomatology (P=0.002). Our results show that the effects of TCEs on the prospectively assessed recurrence of MDD and self-reported depressive symptoms in the general population depend on the MTHFR genotype. In conclusion, T-allele carriers may be at an increased risk for depressive symptoms or MDD recurrence after exposure to childhood trauma.

  10. Application of Receiver Operating Characteristic Analysis to Refine the Prediction of Potential Digoxin Drug Interactions

    OpenAIRE

    Ellens, Harma; Deng, Shibing; Coleman, JoAnn; Bentz, Joe; Taub, Mitchell E.; Ragueneau-Majlessi, Isabelle; Chung, Sophie P.; Herédi-Szabó, Krisztina; Neuhoff, Sibylle; Palm, Johan; Balimane, Praveen; Zhang, Lei; Jamei, Masoud; Hanna, Imad; O’Connor, Michael

    2013-01-01

    In the 2012 Food and Drug Administration (FDA) draft guidance on drug-drug interactions (DDIs), a new molecular entity that inhibits P-glycoprotein (P-gp) may need a clinical DDI study with a P-gp substrate such as digoxin when the maximum concentration of inhibitor at steady state divided by IC50 ([I1]/IC50) is ≥0.1 or concentration of inhibitor based on highest approved dose dissolved in 250 ml divide by IC50 ([I2]/IC50) is ≥10. In this article, refined criteria are presented, determined by...

  11. Model Predictive Control of A Matrix-Converter Based Solid State Transformer for Utility Grid Interaction

    Energy Technology Data Exchange (ETDEWEB)

    Xue, Yaosuo [ORNL

    2016-01-01

    The matrix converter solid state transformer (MC-SST), formed from the back-to-back connection of two three-to-single-phase matrix converters, is studied for use in the interconnection of two ac grids. The matrix converter topology provides a light weight and low volume single-stage bidirectional ac-ac power conversion without the need for a dc link. Thus, the lifetime limitations of dc-bus storage capacitors are avoided. However, space vector modulation of this type of MC-SST requires to compute vectors for each of the two MCs, which must be carefully coordinated to avoid commutation failure. An additional controller is also required to control power exchange between the two ac grids. In this paper, model predictive control (MPC) is proposed for an MC-SST connecting two different ac power grids. The proposed MPC predicts the circuit variables based on the discrete model of MC-SST system and the cost function is formulated so that the optimal switch vector for the next sample period is selected, thereby generating the required grid currents for the SST. Simulation and experimental studies are carried out to demonstrate the effectiveness and simplicity of the proposed MPC for such MC-SST-based grid interfacing systems.

  12. INTERACTIONS BETWEEN SUMMER SUBTROPICAL HIGH AND SST AND PREDICTION OF SUBTROPICAL HIGH

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Most of the study on correlation between the subtropical high and SST focus on the location or intensity of the former versus the latter. It is worthwhile to work on links other than what is usually addressed to identify guidelines in the prediction of the subtropical high. Based on the analysis of subtropical high in west Pacific summer and its correlation with SST in equatorial and north Pacific from previous December to subsequent November, correlation between the area index, west-extending point, location of the subtropical high ridge and SST is discussed. It is conducted by looking into the confidence level of gridpoints and their percentage in the total and examining how they vary with time. From the point of intensity and movement/expansion, feedbacks from the subtropical high to SST are also studied. The SST affects the subtropical high just as the subtropical high affects the SST. A linkage model is thus set up to assist the making of summer rainfall prediction in China's raining seasons.

  13. Dorsomedial prefrontal cortex mediates rapid evaluations predicting the outcome of romantic interactions.

    Science.gov (United States)

    Cooper, Jeffrey C; Dunne, Simon; Furey, Teresa; O'Doherty, John P

    2012-11-07

    Humans frequently make real-world decisions based on rapid evaluations of minimal information; for example, should we talk to an attractive stranger at a party? Little is known, however, about how the brain makes rapid evaluations with real and immediate social consequences. To address this question, we scanned participants with functional magnetic resonance imaging (fMRI) while they viewed photos of individuals that they subsequently met at real-life "speed-dating" events. Neural activity in two areas of dorsomedial prefrontal cortex (DMPFC), paracingulate cortex, and rostromedial prefrontal cortex (RMPFC) was predictive of whether each individual would be ultimately pursued for a romantic relationship or rejected. Activity in these areas was attributable to two distinct components of romantic evaluation: either consensus judgments about physical beauty (paracingulate cortex) or individualized preferences based on a partner's perceived personality (RMPFC). These data identify novel computational roles for these regions of the DMPFC in even very rapid social evaluations. Even a first glance, then, can accurately predict romantic desire, but that glance involves a mix of physical and psychological judgments that depend on specific regions of DMPFC.

  14. Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions with Efavirenz Involving Simultaneous Inducing and Inhibitory Effects on Cytochromes.

    Science.gov (United States)

    Marzolini, Catia; Rajoli, Rajith; Battegay, Manuel; Elzi, Luigia; Back, David; Siccardi, Marco

    2017-04-01

    Antiretroviral drugs are among the therapeutic agents with the highest potential for drug-drug interactions (DDIs). In the absence of clinical data, DDIs are mainly predicted based on preclinical data and knowledge of the disposition of individual drugs. Predictions can be challenging, especially when antiretroviral drugs induce and inhibit multiple cytochrome P450 (CYP) isoenzymes simultaneously. This study predicted the magnitude of the DDI between efavirenz, an inducer of CYP3A4 and inhibitor of CYP2C8, and dual CYP3A4/CYP2C8 substrates (repaglinide, montelukast, pioglitazone, paclitaxel) using a physiologically based pharmacokinetic (PBPK) modeling approach integrating concurrent effects on CYPs. In vitro data describing the physicochemical properties, absorption, distribution, metabolism, and elimination of efavirenz and CYP3A4/CYP2C8 substrates as well as the CYP-inducing and -inhibitory potential of efavirenz were obtained from published literature. The data were integrated in a PBPK model developed using mathematical descriptions of molecular, physiological, and anatomical processes defining pharmacokinetics. Plasma drug-concentration profiles were simulated at steady state in virtual individuals for each drug given alone or in combination with efavirenz. The simulated pharmacokinetic parameters of drugs given alone were compared against existing clinical data. The effect of efavirenz on CYP was compared with published DDI data. The predictions indicate that the overall effect of efavirenz on dual CYP3A4/CYP2C8 substrates is induction of metabolism. The magnitude of induction tends to be less pronounced for dual CYP3A4/CYP2C8 substrates with predominant CYP2C8 metabolism. PBPK modeling constitutes a useful mechanistic approach for the quantitative prediction of DDI involving simultaneous inducing or inhibitory effects on multiple CYPs as often encountered with antiretroviral drugs.

  15. Wind-US Code Contributions to the First AIAA Shock Boundary Layer Interaction Prediction Workshop

    Science.gov (United States)

    Georgiadis, Nicholas J.; Vyas, Manan A.; Yoder, Dennis A.

    2013-01-01

    This report discusses the computations of a set of shock wave/turbulent boundary layer interaction (SWTBLI) test cases using the Wind-US code, as part of the 2010 American Institute of Aeronautics and Astronautics (AIAA) shock/boundary layer interaction workshop. The experiments involve supersonic flows in wind tunnels with a shock generator that directs an oblique shock wave toward the boundary layer along one of the walls of the wind tunnel. The Wind-US calculations utilized structured grid computations performed in Reynolds-averaged Navier-Stokes mode. Four turbulence models were investigated: the Spalart-Allmaras one-equation model, the Menter Baseline and Shear Stress Transport k-omega two-equation models, and an explicit algebraic stress k-omega formulation. Effects of grid resolution and upwinding scheme were also considered. The results from the CFD calculations are compared to particle image velocimetry (PIV) data from the experiments. As expected, turbulence model effects dominated the accuracy of the solutions with upwinding scheme selection indicating minimal effects.

  16. InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information.

    Science.gov (United States)

    Yu, Jinchao; Vavrusa, Marek; Andreani, Jessica; Rey, Julien; Tufféry, Pierre; Guerois, Raphaël

    2016-07-01

    The structural modeling of protein-protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/.

  17. Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM

    Directory of Open Access Journals (Sweden)

    Zhen-Guo Gao

    2016-01-01

    Full Text Available Protein-Protein Interactions (PPIs play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF algorithm combined with Autocovariance (AC features extracted from the Position-Specific Scoring Matrix (PSSM. More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.

  18. Testosterone dynamics and psychopathic personality traits independently predict antagonistic behavior towards the perceived loser of a competitive interaction.

    Science.gov (United States)

    Geniole, Shawn N; Busseri, Michael A; McCormick, Cheryl M

    2013-11-01

    Few studies have investigated the influence of changes in testosterone on subsequent competitive, antagonistic behavior in humans. Further, little is known about the extent to which such effects are moderated by personality traits. Here, we collected salivary measures of testosterone before and after a rigged competition. After the competition, participants were given the opportunity to act antagonistically against the competitor (allocate a low honorarium). We hypothesized that changes in testosterone throughout the competition would predict antagonistic behavior such that greater increases would be associated with the allocation of lower honorariums. Further, we investigated the extent to which personality traits related to psychopathy (fearless dominance, FD; self-centered impulsivity, SCI; and coldheartedness) moderated this relationship. In men (n=104), greater increases in testosterone and greater FD were associated with more antagonistic behavior, but testosterone concentrations did not interact with personality measures. In women (n=97), greater FD and SCI predicted greater antagonistic behavior, but there were no significant endocrine predictors or interactions with personality measures. In a secondary set of analyses, we found no support for the dual-hormone hypothesis that the relationship between baseline testosterone concentrations and behavior is moderated by cortisol concentrations. Thus, results are consistent with previous findings that in men, situation-specific testosterone reactivity rather than baseline endocrine function is a better predictor of future antagonistic behavior. The results are discussed with respect to the Challenge Hypothesis and the Biosocial Model of Status, and the possible mechanisms underlying the independent relations of testosterone and personality factors with antagonistic behavior.

  19. 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.

  20. Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions

    DEFF Research Database (Denmark)

    Bøgebo, Rikke; Horn, Heiko; Olsen, Jesper V;

    2014-01-01

    -arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT1aR. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data......Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT1aR) is one of the most studied 7TMRs with respect to selective activation of the β...... likely activated kinases. This suggested that AT1aR-dependent signalling activates 48 of the 285 kinases detected in HEK293 cells. Of these, Aurora B, CLK3 and PKG1 have not previously been described in the pathway whereas others, such as PKA, PKB and PKC, are well known. In summary, we have developed...

  1. Incarceration and Unstable Housing Interact to Predict Sexual Risk Behaviors among African American STD Clinic Patients

    Science.gov (United States)

    Widman, Laura; Noar, Seth M.; Golin, Carol E.; Willoughby, Jessica Fitts; Crosby, Richard

    2014-01-01

    Given dramatic racial disparities in rates of HIV/STDs among African Americans, understanding broader structural factors that increase the risk for HIV/STDs is crucial. This study investigated incarceration history and unstable housing as two structural predictors of HIV risk behavior among 293 African Americans (159 men/134 women, Mage=27). Participants were recruited from an urban STD clinic in the southeastern U.S. Approximately half the sample had been incarcerated in their lifetime (54%), and 43% had been unstably housed in the past 6 months. Incarceration was independently associated with number of sex partners and the frequency of unprotected sex. Unstable housing was independently associated with the frequency of unprotected sex. However, these main effects were qualified by significant interactions: individuals with a history of incarceration and more unstable housing had more sex partners and more unprotected sex in the past three months than individuals without these structural barriers. Implications for structural-level interventions are discussed. PMID:24060677

  2. Simulation and Prediction of Wakes and Wake Interaction in Wind Farms

    DEFF Research Database (Denmark)

    Andersen, Søren Juhl

    The highly turbulent wake and the wake interaction of merging wakes between multiple wind turbines are modelled using Large Eddy Simulation (LES) in a general Navier-Stokes solver. The Actuator Line (AL) technique is employed to model the wind turbines, and the aeroelastic computations are fully...... coupled with the flow solver. The numerical simulations include the study of the far wake behind a single turbine, three idealised cases of infinitely long rows of turbines and finally three infinite wind farm scenarios with different spacings. The flow characteristics between the turbines, turbine...... performance, and principal turbulent quantities are examined for the different scenarios. The study focuses on the large coherent structures and movements of the wake behind and between wind turbines. The large coherent structures are analysed using Proper Orthogonal Decoposition (POD). POD constitutes...

  3. 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.

  4. Prediction of bioactive compound pathways using chemical interaction and structural information.

    Science.gov (United States)

    Cheng, Shiwen; Zhu, Changming; Chu, Chen; Huang, Tao; Kong, Xiangyin; Zhu, Liu Cun

    2016-01-01

    The functional screening of compounds is an important topic in chemistry and biomedicine that can uncover the essential properties of compounds and provide information concerning their correct use. In this study, we investigated the bioactive compounds reported in Selleckchem, which were assigned to 22 pathways. A computational method was proposed to identify the pathways of the bioactive compounds. Unlike most existing methods that only consider compound structural information, the proposed method adopted both the structural and interaction information from the compounds. The total accuracy achieved by our method was 61.79% based on jackknife analysis of a dataset of 1,832 bioactive compounds. Its performance was quite good compared with that of other machine learning algorithms (with total accuracies less than 46%). Finally, some of the false positives obtained by the method were analyzed to investigate the likelihood of compounds being annotated to new pathways.

  5. iLIR: A web resource for prediction of Atg8-family interacting proteins.

    Science.gov (United States)

    Kalvari, Ioanna; Tsompanis, Stelios; Mulakkal, Nitha C; Osgood, Richard; Johansen, Terje; Nezis, Ioannis P; Promponas, Vasilis J

    2014-05-01

    Macroautophagy was initially considered to be a nonselective process for bulk breakdown of cytosolic material. However, recent evidence points toward a selective mode of autophagy mediated by the so-called selective autophagy receptors (SARs). SARs act by recognizing and sorting diverse cargo substrates (e.g., proteins, organelles, pathogens) to the autophagic machinery. Known SARs are characterized by a short linear sequence motif (LIR-, LRS-, or AIM-motif) responsible for the interaction between SARs and proteins of the Atg8 family. Interestingly, many LIR-containing proteins (LIRCPs) are also involved in autophagosome formation and maturation and a few of them in regulating signaling pathways. Despite recent research efforts to experimentally identify LIRCPs, only a few dozen of this class of-often unrelated-proteins have been characterized so far using tedious cell biological, biochemical, and crystallographic approaches. The availability of an ever-increasing number of complete eukaryotic genomes provides a grand challenge for characterizing novel LIRCPs throughout the eukaryotes. Along these lines, we developed iLIR, a freely available web resource, which provides in silico tools for assisting the identification of novel LIRCPs. Given an amino acid sequence as input, iLIR searches for instances of short sequences compliant with a refined sensitive regular expression pattern of the extended LIR motif (xLIR-motif) and retrieves characterized protein domains from the SMART database for the query. Additionally, iLIR scores xLIRs against a custom position-specific scoring matrix (PSSM) and identifies potentially disordered subsequences with protein interaction potential overlapping with detected xLIR-motifs. Here we demonstrate that proteins satisfying these criteria make good LIRCP candidates for further experimental verification. Domain architecture is displayed in an informative graphic, and detailed results are also available in tabular form. We anticipate

  6. FKBP5 genotype interacts with early life trauma to predict heavy drinking in college students.

    Science.gov (United States)

    Lieberman, Richard; Armeli, Stephen; Scott, Denise M; Kranzler, Henry R; Tennen, Howard; Covault, Jonathan

    2016-09-01

    Alcohol use disorder (AUD) is debilitating and costly. Identification and better understanding of risk factors influencing the development of AUD remain a research priority. Although early life exposure to trauma increases the risk of adulthood psychiatric disorders, including AUD, many individuals exposed to early life trauma do not develop psychopathology. Underlying genetic factors may contribute to differential sensitivity to trauma experienced in childhood. The hypothalamic-pituitary-adrenal (HPA) axis is susceptible to long-lasting changes in function following childhood trauma. Functional genetic variation within FKBP5, a gene encoding a modulator of HPA axis function, is associated with the development of psychiatric symptoms in adulthood, particularly among individuals exposed to trauma early in life. In the current study, we examined interactions between self-reported early life trauma, past-year life stress, past-year trauma, and a single nucleotide polymorphism (rs1360780) in FKBP5 on heavy alcohol consumption in a sample of 1,845 college students from two university settings. Although we found no effect of early life trauma on heavy drinking in rs1360780*T-allele carriers, rs1360780*C homozygotes exposed to early life trauma had a lower probability of heavy drinking compared to rs1360780*C homozygotes not exposed to early life trauma (P stress or past-year trauma, and FKBP5 genotype on heavy drinking suggests that there exists a developmental period of susceptibility to stress that is moderated by FKBP5 genotype. These findings implicate interactive effects of early life trauma and FKBP5 genetic variation on heavy drinking. © 2016 Wiley Periodicals, Inc.

  7. Cognitive model of trust dynamics predicts human behavior within and between two games of strategic interaction with computerized confederate agents

    Directory of Open Access Journals (Sweden)

    Michael Gordon Collins

    2016-02-01

    Full Text Available 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. 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.

  9. Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners.

    Directory of Open Access Journals (Sweden)

    Carlo Baldassi

    Full Text Available In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids, exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i the prediction of residue-residue contacts in proteins, and (ii the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.

  10. Predictive relationships between chronic pain and negative emotions: a 4-month daily process study using Therapeutic Interactive Voice Response (TIVR).

    Science.gov (United States)

    Naylor, Magdalena R; Krauthamer, G Michael; Naud, Shelly; Keefe, Francis J; Helzer, John E

    2011-01-01

    This article examines temporal relationships between negative emotions and pain in a cohort of 33 patients with chronic musculoskeletal pain enrolled in a telephone-based relapse prevention program (Therapeutic Interactive Voice Response [TIVR]), after 11 weeks of group cognitive behavioral therapy (CBT). Patients were asked to make daily reports to the TIVR system for 4 months after CBT. Patients' daily reports were analyzed with path analysis to examine temporal relationships between 3 emotion variables (anger, sadness, and stress) and 2 pain variables (pain and pain control). As expected, same-day correlations were significant between emotion variables and both pain and pain control. The lagged associations revealed unidirectional relationships between pain and next-day emotions: increased pain predicted higher reports of sadness the following day (P pain control predicted decreased sadness and anger the following day (P emotions predicted increased next-day pain. We speculate that CBT treatment followed by the relapse prevention program teaches patients how to modulate negative emotions such that they no longer have a negative impact on next-day pain perception. The clinical implications of our findings are discussed.

  11. 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.

  12. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

    Directory of Open Access Journals (Sweden)

    Fangrui Ding

    Full Text Available 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

  13. 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.

  14. Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps

    Directory of Open Access Journals (Sweden)

    Vasconcelos Ana Tereza R

    2008-06-01

    Full Text Available Abstract Background Little is known about bacterial transcriptional regulatory networks (TRNs. In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only ~50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms. Results We designed feed-forward (FF and bi-fan (BF motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs. The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested. Conclusion The motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when

  15. Towards Predictive Modeling of Information Processing in Microbial Ecosystems With Quorum-Sensing Interactions

    Science.gov (United States)

    Yusufaly, Tahir; Boedicker, James

    Bacteria communicate using external chemical signals in a process known as quorum sensing. However, the efficiency of this communication is reduced by both limitations on the rate of diffusion over long distances and potential interference from neighboring strains. Therefore, having a framework to quantitatively predict how spatial structure and biodiversity shape information processing in bacterial colonies is important, both for understanding the evolutionary dynamics of natural microbial ecosystems, and for the rational design of synthetic ecosystems with desired computational properties. As a first step towards these goals, we implement a reaction-diffusion model to study the dynamics of a LuxI/LuxR quorum sensing circuit in a growing bacterial population. The spatiotemporal concentration profile of acyl-homoserine lactone (AHL) signaling molecules is analyzed, and used to define a measure of physical and functional signaling network connectivity. From this, we systematically investigate how different initial distributions of bacterial populations influence the subsequent efficiency of collective long-range signal propagation in the population. We compare our results with known experimental data, and discuss limitations and extensions to our modeling framework.-/abstract-

  16. Anxiety Interacts With Expressed Emotion Criticism in the Prediction of Psychotic Symptom Exacerbation

    Science.gov (United States)

    Docherty, Nancy M.; St-Hilaire, Annie; Aakre, Jennifer M.; Seghers, James P.; McCleery, Amanda; Divilbiss, Marielle

    2011-01-01

    Psychotic symptoms are exacerbated by social stressors in schizophrenia and schizoaffective disorder patients as a group. More specifically, critical attitudes toward patients on the part of family members and others have been associated with a higher risk of relapse in the patients. Some patients appear to be especially vulnerable in this regard. One variable that could affect the degree of sensitivity to a social stressor such as criticism is the individual’s level of anxiety. The present longitudinal study assessed 27 relatively stable outpatients with schizophrenia or schizoaffective disorder and the single “most influential other” (MIO) person for each patient. As hypothesized, (1) patients with high critical MIOs showed increases in psychotic symptoms over time, compared with patients with low critical MIOs; (2) patients high in anxiety at the baseline assessment showed increases in psychotic symptoms at follow-up, compared with patients low in anxiety, and (3) patients with high levels of anxiety at baseline and high critical MIOs showed the greatest exacerbation of psychotic symptoms over time. Objectively measured levels of criticism were more predictive than patient-rated levels of criticism. PMID:19892819

  17. Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers.

    Science.gov (United States)

    Rabin, Borsika A; Gaglio, Bridget; Sanders, Tristan; Nekhlyudov, Larissa; Dearing, James W; Bull, Sheana; Glasgow, Russell E; Marcus, Alfred

    2013-10-01

    Cancer prognosis is of keen interest for patients with cancer, their caregivers, and providers. Prognostic tools have been developed to guide patient-physician communication and decision-making. Given the proliferation of prognostic tools, it is timely to review existing online cancer prognostic tools and discuss implications for their use in clinical settings. Using a systematic approach, we searched the Internet, Medline, and consulted with experts to identify existing online prognostic tools. Each was reviewed for content and format. Twenty-two prognostic tools addressing 89 different cancers were identified. Tools primarily focused on prostate (n = 11), colorectal (n = 10), breast (n = 8), and melanoma (n = 6), although at least one tool was identified for most malignancies. The input variables for the tools included cancer characteristics (n = 22), patient characteristics (n = 18), and comorbidities (n = 9). Effect of therapy on prognosis was included in 15 tools. The most common predicted outcome was cancer-specific survival/mortality (n = 17). Only a few tools (n = 4) suggested patients as potential target users. A comprehensive repository of online prognostic tools was created to understand the state-of-the-art in prognostic tool availability and characteristics. Use of these tools may support communication and understanding about cancer prognosis. Dissemination, testing, refinement of existing, and development of new tools under different conditions are needed.

  18. 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…

  19. 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.

  20. Benzene-pyridine interactions predicted by the effective fragment potential method.

    Science.gov (United States)

    Smith, Quentin A; Gordon, Mark S; Slipchenko, Lyudmila V

    2011-05-12

    The accurate representation of nitrogen-containing heterocycles is essential for modeling biological systems. In this study, the general effective fragment potential (EFP2) method is used to model dimers of benzene and pyridine, complexes for which high-level theoretical data -including large basis spin-component-scaled second-order perturbation theory (SCS-MP2), symmetry-adapted perturbation theory (SAPT), and coupled cluster with singles, doubles, and perturbative triples (CCSD(T))-are available. An extensive comparison of potential energy curves and components of the interaction energy is presented for sandwich, T-shaped, parallel displaced, and hydrogen-bonded structures of these dimers. EFP2 and CCSD(T) potential energy curves for the sandwich, T-shaped, and hydrogen-bonded dimers have an average root-mean-square deviation (RMSD) of 0.49 kcal/mol; EFP2 and SCS-MP2 curves for the parallel displaced dimers have an average RMSD of 0.52 kcal/mol. Additionally, results are presented from an EFP2 Monte Carlo/simulated annealing (MC/SA) computation to sample the potential energy surface of the benzene-pyridine and pyridine dimers.

  1. The Core Extrusion Schema-Revised: Hiding Oneself Predicts Severity of Social Interaction Anxiety.

    Science.gov (United States)

    Levinson, Cheri A; Rodebaugh, Thomas L; Lim, Michelle H; Fernandez, Katya C

    2017-01-01

    Cognitive behavioral models of social anxiety disorder (SAD) suggest that fear of negative evaluation is a core fear or vulnerability for SAD. However, why negative evaluation is feared is not fully understood. It is possible that core beliefs contribute to the relationship between fear of negative evaluation and SAD. One of these beliefs may be a core extrusion schema: a constellation of beliefs that one's true self will be rejected by others and therefore one should hide one's true self. In the current study (N = 699), we extended research on the Core Extrusion Schema and created a shortened and revised version of the measure called the Core Extrusion Schema-Revised The Core Extrusion Schema-Revised demonstrated good factor fit for its two subscales (Hidden Self and Rejection of the True Self) and was invariant across gender and ethnicity. The Hidden Self subscale demonstrated excellent incremental validity within the full sample as well as in participants diagnosed with generalized SAD. Specifically, the Hidden Self subscale may help explain severity of social interaction anxiety. This measure could be used with individuals diagnosed with generalized SAD to design exposures targeting these core beliefs.

  2. Computational Capabilities for Predictions of Interactions at the Grain Boundary of Refractory Alloys

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Debasis; Kwak, Shaun; Vasenkov, Alex; Shin, Yun Kyung; Duin, Adri van

    2014-09-30

    New high performance refractory alloys are critically required for improving efficiency and decreasing CO2 emissions of fossil energy systems. The development of these materials remains slow because it is driven by a trial-and-error experimental approach and lacks a rational design approach. Atomistic Molecular Dynamic (MD) design has the potential to accelerate this development through the prediction of mechanical properties and corrosion resistance of new materials. The success of MD simulations depends critically on the fidelity of interatomic potentials. This project, in collaboration with Penn State, has focused on developing and validating high quality quantum mechanics based reactive potentials, ReaxFF, for Ni-Fe-Al-Cr-O-S system. A larger number of accurate density functional theory (DFT) calculations were performed to generate data for parameterizing the ReaxFF potentials. These potentials were then used in molecular dynamics (MD) and molecular dynamics-Monte Carlo (MD-MC) for much larger system to study for which DFT calculation would be prohibitively expensive, and to understand a number of chemical phenomena Ni-Fe-Al-Cr-O-S based alloy systems . These include catalytic oxidation of butane on clean Cr2O3 and pyrite/Cr2O3, interfacial reaction between Cr2O3 (refractory material) and Al2O3 (slag), cohesive strength of at the grain boundary of S-enriched Cr compared to bulk Cr and Ssegregation study in Al, Al2O3, Cr and Cr2O3 with a grain structure. The developed quantum based ReaxFF potential are available from the authors upon request. During this project, a number of papers were published in peer-reviewed journals. In addition, several conference presentations were made.

  3. Computational Capabilities for Predictions of Interactions at the Grain Boundary of Refractory Alloys

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Debasis; Kwak, Shaun; Vasenkov, Alex; Shin, Yun Kyung; Duin, Adri van

    2014-09-30

    New high performance refractory alloys are critically required for improving efficiency and decreasing CO2 emissions of fossil energy systems. The development of these materials remains slow because it is driven by a trial-and-error experimental approach and lacks a rational design approach. Atomistic Molecular Dynamic (MD) design has the potential to accelerate this development through the prediction of mechanical properties and corrosion resistance of new materials. The success of MD simulations depends critically on the fidelity of interatomic potentials. This project, in collaboration with Penn State, has focused on developing and validating high quality quantum mechanics based reactive potentials, ReaxFF, for Ni-Fe-Al-Cr-O-S system. A larger number of accurate density functional theory (DFT) calculations were performed to generate data for parameterizing the ReaxFF potentials. These potentials were then used in molecular dynamics (MD) and molecular dynamics-Monte Carlo (MD-MC) for much larger system to study for which DFT calculation would be prohibitively expensive, and to understand a number of chemical phenomena Ni-Fe-Al-Cr-O-S based alloy systems . These include catalytic oxidation of butane on clean Cr2O3 and pyrite/Cr2O3, interfacial reaction between Cr2O3 (refractory material) and Al2O3 (slag), cohesive strength of at the grain boundary of S-enriched Cr compared to bulk Cr and Ssegregation study in Al, Al2O3, Cr and Cr2O3 with a grain structure. The developed quantum based ReaxFF potential are available from the authors upon request. During this project, a number of papers were published in peer-reviewed journals. In addition, several conference presentations were made.

  4. Computational Capabilities for Predictions of Interactions at the Grain Boundary of Refractory Alloys

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Debasis [CFD Research Corporation, Huntsville, AL (United States); Kwak, Shaun [CFD Research Corporation, Huntsville, AL (United States); Vasenkov, Alex [CFD Research Corporation, Huntsville, AL (United States); Shin, Yun Kyung [Pennsylvania State Univ., University Park, PA (United States); Duin, Adri van [Pennsylvania State Univ., University Park, PA (United States)

    2014-12-01

    New high performance refractory alloys are critically required for improving efficiency and decreasing CO2 emissions of fossil energy systems. The development of these materials remains slow because it is driven by a trial-and-error experimental approach and lacks a rational design approach. Atomistic Molecular Dynamic (MD) design has the potential to accelerate this development through the prediction of mechanical properties and corrosion resistance of new materials. The success of MD simulations depends critically on the fidelity of interatomic potentials. This project, in collaboration with Penn State, has focused on developing and validating high quality quantum mechanics based reactive potentials, ReaxFF, for Ni-Fe-Al-Cr-O-S system. A larger number of accurate density functional theory (DFT) calculations were performed to generate data for parameterizing the ReaxFF potentials. These potentials were then used in molecular dynamics (MD) and molecular dynamics-Monte Carlo (MD-MC) for much larger system to study for which DFT calculation would be prohibitively expensive, and to understand a number of chemical phenomena Ni-Fe-Al-Cr-O-S based alloy systems . These include catalytic oxidation of butane on clean Cr2O3 and pyrite/Cr2O3, interfacial reaction between Cr2O3 (refractory material) and Al2O3 (slag), cohesive strength of at the grain boundary of S-enriched Cr compared to bulk Cr and Ssegregation study in Al, Al2O3, Cr and Cr2O3 with a grain structure. The developed quantum based ReaxFF potential are available from the authors upon request. During this project, a number of papers were published in peer-reviewed journals. In addition, several conference presentations were made.

  5. Delineating ion-ion interactions by electrostatic modeling for predicting rhizotoxicity of metal mixtures to lettuce Lactuca sativa.

    Science.gov (United States)

    Le, T T Yen; Wang, Peng; Vijver, Martina G; Kinraide, Thomas B; Hendriks, A Jan; Peijnenburg, Willie J G M

    2014-09-01

    Effects of ion-ion interactions on metal toxicity to lettuce Lactuca sativa were studied based on the electrical potential at the plasma membrane surface (ψ0 ). Surface interactions at the proximate outside of the membrane influenced ion activities at the plasma membrane surface ({M(n+)}0). At a given free Cu(2+) activity in the bulk medium ({Cu(2+)}b), additions of Na(+), K(+), Ca(2+), and Mg(2+) resulted in substantial decreases in {Cu(2+)}0. Additions of Zn(2+) led to declines in {Cu(2+)}0, but Cu(2+) and Ag(+) at the exposure levels tested had negligible effects on the plasma membrane surface activity of each other. Metal toxicity was expressed by the {M(n+)}0 -based strength coefficient, indicating a decrease of toxicity in the order: Ag(+)  > Cu(2+)  > Zn(2+). Adsorbed Na(+), K(+), Ca(2+), and Mg(2+) had significant and dose-dependent effects on Cu(2+) toxicity in terms of osmolarity. Internal interactions between Cu(2+) and Zn(2+) and between Cu(2+) and Ag(+) were modeled by expanding the strength coefficients in concentration addition and response multiplication models. These extended models consistently indicated that Zn(2+) significantly alleviated Cu(2+) toxicity. According to the extended concentration addition model, Ag(+) significantly enhanced Cu(2+) toxicity whereas Cu(2+) reduced Ag(+) toxicity. By contrast, the response multiplication model predicted insignificant effects of adsorbed Cu(2+) and Ag(+) on the toxicity of each other. These interactions were interpreted using ψ0, demonstrating its influence on metal toxicity.

  6. Hidden sector hydrogen as dark matter: Small-scale structure formation predictions and the importance of hyperfine interactions

    Science.gov (United States)

    Boddy, Kimberly K.; Kaplinghat, Manoj; Kwa, Anna; Peter, Annika H. G.

    2016-12-01

    We study the atomic physics and the astrophysical implications of a model in which the dark matter is the analog of hydrogen in a secluded sector. The self-interactions between dark matter particles include both elastic scatterings as well as inelastic processes due to a hyperfine transition. The self-interaction cross sections are computed by numerically solving the coupled Schrödinger equations for this system. We show that these self-interactions exhibit the right velocity dependence to explain the low dark matter density cores seen in small galaxies while being consistent with all constraints from observations of clusters of galaxies. For a viable solution, the dark hydrogen mass has to be in the 10-100 GeV range and the dark fine-structure constant has to be larger than 0.01. This range of model parameters requires the existence of a dark matter-antimatter asymmetry in the early universe to set the relic abundance of dark matter. For this range of parameters, we show that significant cooling losses may occur due to inelastic excitations to the hyperfine state and subsequent decays, with implications for the evolution of low-mass halos and the early growth of supermassive black holes. Cooling from excitations to higher n levels of dark hydrogen and subsequent decays is possible at the cluster scale, with a strong dependence on halo mass. Finally, we show that the minimum halo mass is in the range of 1 03.5 to 1 07M⊙ for the viable regions of parameter space, significantly larger than the typical predictions for weakly interacting dark matter models. This pattern of observables in cosmological structure formation is unique to this model, making it possible to rule in or rule out hidden sector hydrogen as a viable dark matter model.

  7. Effector prediction in host-pathogen interaction based on a Markov model of a ubiquitous EPIYA motif

    Science.gov (United States)

    2010-01-01

    Background Effector secretion is a common strategy of pathogen in mediating host-pathogen interaction. Eight EPIYA-motif containing effectors have recently been discovered in six pathogens. Once these effectors enter host cells through type III/IV secretion systems (T3SS/T4SS), tyrosine in the EPIYA motif is phosphorylated, which triggers effectors binding other proteins to manipulate host-cell functions. The objectives of this study are to evaluate the distribution pattern of EPIYA motif in broad biological species, to predict potential effectors with EPIYA motif, and to suggest roles and biological functions of potential effectors in host-pathogen interactions. Results A hidden Markov model (HMM) of five amino acids was built for the EPIYA-motif based on the eight known effectors. Using this HMM to search the non-redundant protein database containing 9,216,047 sequences, we obtained 107,231 sequences with at least one EPIYA motif occurrence and 3115 sequences with multiple repeats of the EPIYA motif. Although the EPIYA motif exists among broad species, it is significantly over-represented in some particular groups of species. For those proteins containing at least four copies of EPIYA motif, most of them are from intracellular bacteria, extracellular bacteria with T3SS or T4SS or intracellular protozoan parasites. By combining the EPIYA motif and the adjacent SH2 binding motifs (KK, R4, Tarp and Tir), we built HMMs of nine amino acids and predicted many potential effectors in bacteria and protista by the HMMs. Some potential effectors for pathogens (such as Lawsonia intracellularis, Plasmodium falciparum and Leishmania major) are suggested. Conclusions Our study indicates that the EPIYA motif may be a ubiquitous functional site for effectors that play an important pathogenicity role in mediating host-pathogen interactions. We suggest that some intracellular protozoan parasites could secrete EPIYA-motif containing effectors through secretion systems similar to the

  8. Prediction of the diffuse neutrino flux from cosmic ray interactions near supernova remnants

    Science.gov (United States)

    Mandelartz, Matthias; Becker Tjus, Julia

    2015-05-01

    In this paper, we present high-energy neutrino spectra from 21 Galactic supernova remnants (SNRs), derived from gamma-ray measurements in the GeV-TeV range. We find that only the strongest sources, i.e. G40.5-0.5 in the north and Vela Junior in the south could be detected as single point sources by IceCube or KM3NeT, respectively. For the first time, it is also possible to derive a diffuse signal by applying the observed correlation between gamma-ray emission and radio signal. Radio data from 234 supernova remnants listed in Green's catalog are used to show that the total diffuse neutrino flux is approximately a factor of 2.5 higher compared to the sources that are resolved so far. We show that the signal at above 10 TeV energies can actually become comparable to the diffuse neutrino flux component from interactions in the interstellar medium. Recently, the IceCube collaboration announced the detection of a first diffuse signal of astrophysical high-energy neutrinos. Directional information cannot unambiguously reveal the nature of the sources at this point due to low statistics. A number of events come from close to the Galactic center and one of the main questions is whether at least a part of the signal can be of Galactic nature. In this paper, we show that the diffuse flux from well-resolved SNRs is at least a factor of 20 below the observed flux.

  9. Prediction of drug-drug interactions between various antidepressants and ritonavir using a physiologically based pharmacokinetic model

    Directory of Open Access Journals (Sweden)

    M Siccardi

    2012-11-01

    Full Text Available Depression can impact on the treatment of HIV infection, and effective treatment of depressive conditions can have a beneficial effect improving adherence. However antidepressant treatment requires long-term maintenance, and is prone to pharmacokinetic drug-drug interactions (DDI with antiretrovirals. The aim of this study was to predict the magnitude of DDI between ritonavir (RTV and the most commonly prescribed antidepressants using a physiologically based pharmacokinetic (PBPK model simulating virtual clinical trials. In vitro data describing the physiochemical properties, absorption, metabolism, induction and inhibitory potential of RTV and five antidepressants were obtained from published literature. Interactions between RTV and antidepressants were evaluated using the full PBPK model implemented in the Simcyp Population-based Simulator (Version 11.1, Simcyp Limited, UK and virtual clinical studies were simulated on 50 Caucasian subjects receiving 100mg bid of RTV for 21 days plus sertraline (100mg qd, citalopram (40mg qd, fluoxetine (20mg qd, venlafaxine (25mg qd and then from day 14–21. Simulated pharmacokinetic parameters were compared with observed values available in the literature. The simulated PK parameters of RTV, sertraline, citalopram, fluoxetine, mirtazepine and venlafaxine given alone at standard dosage were similar to reference values obtain from published clinical studies. The effect of simulated RTV co-administration on sertaline, fluoxetine and venlaflaxine was an AUC decrease of 40%, 26% and 6%, respectively and on mirtazepine and citalopram, an AUC increase of 60% and 20% respectively. The magnitude of the simulated DDI between RTV and the antidepressants was overall weak to moderate according to the classification of the FDA. The modest magnitude of these drug-drug interactions could be explained by the fact that antidepressants are substrates of multiple isoforms thus metabolism can still occur through CYPs that are

  10. 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...... interaction between a human operator and an artificial actor or agent. We can apply insights from puppetry to develop culturally-aware robots. Here we describe the development of a robotic marionette theatre wherein robotic controllers assume the role of human puppeteers. The system has been built, tested...

  11. An interaction network predicted from public data as a discovery tool: application to the Hsp90 molecular chaperone machine.

    Directory of Open Access Journals (Sweden)

    Pablo C Echeverría

    Full Text Available Understanding the functions of proteins requires information about their protein-protein interactions (PPI. The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community.

  12. Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis.

    Science.gov (United States)

    Piepho, H P

    1994-11-01

    Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.

  13. 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

  14. 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.

  15. Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.

    Science.gov (United States)

    Brender, Jeffrey R; Zhang, Yang

    2015-10-01

    The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.

  16. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.

    Science.gov (United States)

    An, Ji-Yong; You, Zhu-Hong; Meng, Fan-Rong; Xu, Shu-Juan; Wang, Yin

    2016-01-01

    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 a freely

  17. 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.

  18. 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.

  19. 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).

  20. Influences of heterogeneous native contact energy and many-body interactions on the prediction of protein folding mechanisms.

    Science.gov (United States)

    Zhang, Zhuqing; Ouyang, Yanhua; Chen, Tao

    2016-11-16

    Since single-point mutant perturbation has been used to probe protein folding mechanisms in experiments, the ϕ-value has become a critical parameter to infer the transition state (TS) for two-state proteins. Experimentally, large scale analysis has shown a nearly single uniform ϕ-value with normally distributed error from 24 different proteins; moreover, in zero stability conditions, the intrinsic variable ϕ(0) is around 0.36. To explore how and to what extent theoretical models can capture experimental phenomena, we here use structure-based explicit chain coarse-grained models to investigate the influence of single-point mutant perturbation on protein folding for single domain two-state proteins. Our results indicate that uniform, additive contact energetic interactions cannot predict experimental Brønsted plots well. Those points deviate largely from the main data sets in Brønsted plots, are mostly hydrophobic, and are located in N- and C-terminal contacting regions. Heterogenous contact energy, which is dependent on sequence separation, can narrow the point dispersion in a Brønsted plot. Moreover, we demonstrate that combining many-body interactions with heterogeneous native contact energy can present mean ϕ-values consistent with experimental findings, with a comparable distributed error. This indicates that for more accurate elucidation of protein folding mechanisms by residue-level structure-based models, these elements should be considered.

  1. Using ANN to predict E. coli accumulation in coves based on interaction amongst various physical, chemical and biological factors

    Science.gov (United States)

    Dwivedi, D.; Mohanty, B. P.; Lesikar, B. J.

    2008-12-01

    The accumulation of Escherichia Coli (E. coli) in canals, coves and streams is the result of a number of interacting processes operating at multiple spatial and temporal scales. Fate and transport of E. coli in surface water systems is governed by different physical, chemical, and biological processes. Various models developed to quantify each of these processes occurring at different scales are not so far pooled into a single predictive model. At present, very little is known about the fate and transport of E. coli in the environment. We hypothesize that E. coli population heterogeneity in canals and coves is affected by physical factors (average stream width and/ depth, secchi depth, flow and flow severity, day since precipitation, aquatic vegetation, solar radiation, dissolved and total suspended solids etc.); chemical factors (basic water quality, nutrients, organic compounds, pH, and toxicity etc.); and biological factors (type of bacterial strain, predation, and antagonism etc.). The specific objectives of this study are to: (1) examine the interactions between E. coli and various coupled physical, chemical and biological factors; (2) examine the interactions between E. coli and toxic organic pollutants and other pathogens (viruses); and (3) evaluate qualitatively the removal efficiency of E. coli. We suggest that artificial neural networks (ANN) may be used to provide a possible solution to this problem. To demonstrate the application of the approach, we develop an ANN representing E. coli accumulation in two polluted sites at Lake Granbury in the upper part of the Brazos River in North Central Texas. The graphical structure of ANN explicitly represents cause- and-effect relationship between system variables. Each of these relationships can then be quantified independently using an approach suitable for the type and scale of information available. Preliminary results revealed that E. coli concentrations in canals show seasonal variations regardless of change

  2. 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

    2017-02-01

    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.

  3. Interactions

    DEFF Research Database (Denmark)

    The main theme of this anthology is the unique interaction between mathematics, physics and philosophy during the beginning of the 20th century. Seminal theories of modern physics and new fundamental mathematical structures were discovered or formed in this period. Significant physicists...... such as Lorentz and Einstein as well as mathematicians such as Poincare, Minkowski, Hilbert and Weyl contributed to this development. They created the new physical theories and the mathematical disciplines that play such paramount roles in their mathematical formulations. These physicists and mathematicians were...

  4. Systems Biology Analysis of Temporal In vivo Brucella melitensis and Bovine Transcriptomes Predicts host:Pathogen Protein–Protein Interactions

    Science.gov (United States)

    Rossetti, Carlos A.; Drake, Kenneth L.; Lawhon, Sara D.; Nunes, Jairo S.; Gull, Tamara; Khare, Sangeeta; Adams, Leslie G.

    2017-01-01

    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-driven approaches

  5. Gender-enriched transcripts in Haemonchus contortus--predicted functions and genetic interactions based on comparative analyses with Caenorhabditis elegans.

    Science.gov (United States)

    Campbell, Bronwyn E; Nagaraj, Shivashankar H; Hu, Min; Zhong, Weiwei; Sternberg, Paul W; Ong, Eng K; Loukas, Alex; Ranganathan, Shoba; Beveridge, Ian; McInnes, Russell L; Hutchinson, Gareth W; Gasser, Robin B

    2008-01-01

    In the present study, a bioinformatic-microarray approach was employed for the analysis of selected expressed sequence tags (ESTs) from Haemonchus contortus, a key parasitic nematode of small ruminants. Following a bioinformatic analysis of EST data using a semiautomated pipeline, 1885 representative ESTs (rESTs) were selected, to which oligonucleotides (three per EST) were designed and spotted on to a microarray. This microarray was hybridized with cyanine-dye labelled cRNA probes synthesized from RNA from female or male adults of H. contortus. Differential hybridisation was displayed for 301 of the 1885 rESTs ( approximately 16%). Of these, 165 (55%) had significantly greater signal intensities for female cRNA and 136 (45%) for male cRNA. Of these, 113 with increased signals in female or male H. contortus had homologues in Caenorhabditis elegans, predicted to function in metabolism, information storage and processing, cellular processes and signalling, and embryonic and/or larval development. Of the rESTs with no known homologues in C. elegans, 24 ( approximately 40%) had homologues in other nematodes, four had homologues in various other organisms and 30 (52%) had no homology to any sequence in current gene databases. A genetic interaction network was predicted for the C. elegans orthologues of the gender-enriched H. contortus genes, and a focused analysis of a subset revealed a tight network of molecules involved in amino acid, carbohydrate or lipid transport and metabolism, energy production and conversion, translation, ribosomal structure and biogenesis and, importantly, those associated with meiosis and/or mitosis in the germline during oogenesis or spermatogenesis. This study provides a foundation for the molecular, biochemical and functional exploration of selected molecules with differential transcription profiles in H. contortus, for further microarray analyses of transcription in different developmental stages of H. contortus, and for an extended

  6. Predicting the solubility of sulfamethoxypyridazine in individual solvents. II: Relationship between solute-solvent interaction terms and partial solubility parameters.

    Science.gov (United States)

    Martin, A; Bustamante, P; Escalera, B; Sellés, E

    1989-08-01

    In the first paper in the series, an expanded system of parameters was devised to account for orientation and induction effects, and the term Wh was introduced to replace delta 1h delta 2h of the extended Hansen solubility approach. In the present report, a new term, Kh = Wh/delta 1h delta 2h is observed to take on values larger or smaller than unity depending on whether the hydrogen bonded solute-solvent interaction is larger or smaller than predicted by the term delta 1h delta 2h. The acidic delta a and basic delta b solubility parameters are used to represent two parameters, sigma and tau, suggested by Small in his study of proton donor-acceptor properties. The Small equation, including a heat of mixing term for hydrogen bonded species, is shown to be capable of semiquantitative evaluation. A partial molar heat delta H2h of hydrogen bonding is calculated using delta h and Wh terms; delta H2h is found to be correlated with the logarithm of the residual activity coefficient, In alpha R, a term representing strong solute-solvent interaction. The terms Wh, delta H2h, and In alpha 2R may be used to test the deviation from the geometric mean assumed in regular solution theory, and to replace the hydrogen bonding terms of the extended Hansen three-parameter model. The solubility of sulfamethoxypyridazine in 30 solvents is used to test the semiempirical solubility equations. The results are interpreted in terms of partial solubility parameters and the proton donor-acceptor properties of the solvents.

  7. 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.

  8. QCD prediction of jet structure in 2D trigger-associated momentum correlations and implications for multiple parton interactions

    CERN Document Server

    Trainor, Thomas A

    2014-01-01

    The expression "multiple parton interactions" (MPI) denotes a conjectured QCD mechanism representing contributions from secondary (semi)hard parton scattering to the transverse azimuth region (TR) of jet-triggered p-p collisions. MPI is an object of underlying-event (UE) studies that consider variation of TR $n_{ch}$ or $p_t$ yields relative to a trigger condition (leading hadron or jet $p_t$). An alternative approach is 2D trigger-associated (TA) correlations on hadron transverse momentum $p_t$ or rapidity $y_t$ in which all hadrons from all p-p events are included. Based on a two-component (soft+hard) model (TCM) of TA correlations a jet-related TA hard component is isolated. Contributions to the hard component from the triggered dijet and from secondary dijets (MPI) can be distinguished, including their azimuth dependence relative to the trigger direction. Measured $e^+$-$e^-$ and p-\\=p fragmentation functions and a minimum-bias jet spectrum from 200 GeV p-\\=p collisions are convoluted to predict the 2D ha...

  9. 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.

  10. 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.

  11. Catechol-O-methyltransferase Val158met Polymorphism Interacts With Early Experience to Predict Executive Functions in Early Childhood

    Science.gov (United States)

    Blair, Clancy; Sulik, Michael; Willoughby, Michael; Mills-Koonce, Roger; Petrill, Stephen; Bar