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Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.


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Journal ArticleDOI
TL;DR: An interaction network of Escherichia coli, obtained by training a Support Vector Machine on the high quality of interactions in the EcoCyc database, and with the assumption that the periplasmic and cytoplasmic proteins may not interact with each other is reported.
Abstract: Cellular functions are determined by interactions among proteins in the cells. Recognition of these interactions forms an important step in understanding biology at the systems level. Here, we report an interaction network of Escherichia coli, obtained by training a Support Vector Machine on the high quality of interactions in the EcoCyc database, and with the assumption that the periplasmic and cytoplasmic proteins may not interact with each other. The data features included correlation coefficient between bit score phylogenetic profiles, frequency of their co-occurrence in predicted operons, and a new measure-the distance between translational start sites of the genes. The combined genome context methods show a high accuracy of prediction on the test data and predict a total of 78,122 binary interactions. The majority of the interactions identified by high-throughput experimental methods correspond to indirect interaction (interactions through neighbors) in the predicted network. Correlation of the predicted network with the gene essentiality data shows that the essential genes in E. coli exhibit a high linking number, whereas the nonessential genes exhibit a low linking number. Furthermore, our predicted protein-protein interaction network shows that the proteins involved in replication, DNA repair, transcription, translation, and cell wall synthesis are highly connected. We therefore believe that our predicted network will serve as a useful resource in understanding prokaryotic biology.

64 citations

Journal ArticleDOI
TL;DR: This work extracts functional neighborhood features of a gene using Random Walks with Restarts and employs a KNN classifier to predict the function of uncharacterized genes based on the computed neighborhood features, providing a natural control of the trade-off between accuracy and coverage of prediction.
Abstract: The recent advent of high-throughput methods has generated large amounts of gene interaction data. This has allowed the construction of genomewide networks. A significant number of genes in such networks remain uncharacterized and predicting the molecular function of these genes remains a major challenge. A number of existing techniques assume that genes with similar functions are topologically close in the network. Our hypothesis is that genes with similar functions observe similar annotation patterns in their neighborhood, regardless of the distance between them in the interaction network. We thus predict molecular functions of uncharacterized genes by comparing their functional neighborhoods to genes of known function. We propose a two-phase approach. First, we extract functional neighborhood features of a gene using Random Walks with Restarts. We then employ a KNN classifier to predict the function of uncharacterized genes based on the computed neighborhood features. We perform leave-one-out validation experiments on two S. cerevisiae interaction networks and show significant improvements over previous techniques. Our technique provides a natural control of the trade-off between accuracy and coverage of prediction. We further propose and evaluate prediction in sparse genomes by exploiting features from well-annotated genomes.

64 citations

Journal ArticleDOI
TL;DR: PyContact, an easy-to-use, highly flexible, and intuitive graphical user interface-based application, designed to provide a toolkit to investigate biomolecular interactions in MD trajectories, analyzes and visualizes the noncovalent interactions underlying the ion permeation pathway of the human P2X3 receptor.

64 citations

Book
01 May 2005
TL;DR: The ORFeome: the first step toward the interactome of C. elegans, and the future of NLP in Biomedicine.
Abstract: Preface List of Contributors SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION 1 Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquin Dopazo) 11 Data Analysis and Visualization: An Integrative Approach 12 Critical Design and Implementation Factors 13 Overview of Contributions References 2 Biological Databases: Infrastructure, Content and Integration (Allyson L Williams, Paul J Kersey, Manuela Pruess and Rolf Apweiler) 21 Introduction 22 Data Integration 23 Review of Molecular Biology Databases 24 Conclusion References 3 Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquin Dopazo and Haiying Wang) 31 Integrative Data Analysis and Visualization: Motivation and Approaches 32 Integrating Informational Views and Complexity for Understanding Function 33 Integrating Data Analysis Techniques for Supporting Functional Analysis 34 Final Remarks References SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES 4 Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia) 41 Introduction 42 Introduction to Text Mining and NLP 43 Databases and Resources for Biomedical Text Mining 44 Text Mining and Protein-Protein Interactions 45 Other Text-Mining Applications in Genomics 46 The Future of NLP in Biomedicine Acknowledgements References 5 Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis (Long J Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert and Mark Gerstein) 51 Introduction 52 Genomic Features in Protein Interaction Predictions 53 Machine Learning on Protein-Protein Interactions 54 The Missing Value Problem 55 Network Analysis of Protein Interactions 56 Discussion References 6 Integration of Genomic and Phenotypic Data (Amanda Clare) 61 Phenotype 62 Forward Genetics and QTL Analysis 63 Reverse Genetics 64 Prediction of Phenotype from Other Sources of Data 65 Integrating Phenotype Data with Systems Biology 66 Integration of Phenotype Data in Databases 67 Conclusions References 7 Ontologies and Functional Genomics (Fatima Al-Shahrour and Joaquin Dopazo) 71 Information Mining in Genome-Wide Functional Analysis 72 Sources of Information: Free Text Versus Curated Repositories 73 Bio-Ontologies and the Gene Ontology in Functional Genomics 74 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge 75 Statistical Approaches to Test Significant Biological Differences 76 Using FatiGO to Find Significant Functional Associations in Clusters of Genes 77 Other Tools 78 Examples of Functional Analysis of Clusters of Genes 79 Future Prospects References 8 The C elegans Interactome: its Generation and Visualization (Alban Chesnau and Claude Sardet) 81 Introduction 82 The ORFeome: the first step toward the interactome of C elegans 83 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects 84 Visualization and Topology of Protein-Protein Interaction Networks 85 Cross-Talk Between the C elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets 86 Conclusion: From Interactions to Therapies References SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS 9 Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions (Steve R Pettifer, James R Sinnott and Teresa K Attwood) 91 Introduction 92 Sequence Analysis Methods and Databases 93 A View Through a Portal 94 Problems with Monolithic Approaches: One Size Does Not Fit All 95 A Toolkit View 96 Challenges and Opportunities 97 Extending the Desktop Metaphor 98 Conclusions Acknowledgements References 10 Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor) 101 Introduction 102 Some Preliminaries 103 Hierarchical Clustering 104 k-Means Clustering 105 Self-Organizing Maps 106 A Wish List for Clustering Algorithms 107 The Self-Organizing Tree Algorithm 108 Quality-Based Clustering Algorithms 109 Mixture Models 1010 Biclustering Algorithms 1011 Assessing Cluster Quality 1012 Open Horizons References 11 Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G Troyanskaya) 111 Functional Genomics: Goals and Data Sources 112 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data 113 Integration of Diverse Functional Data For Accurate Gene Function Prediction 114 MAGIC - General Probabilistic Integration of Diverse Genomic Data 115 Conclusion References 12 Supervised Methods with Genomic Data: a Review and Cautionary View (Ramon Diaz-Uriarte) 121 Chapter Objectives 122 Class Prediction and Class Comparison 123 Class Comparison: Finding/Ranking Differentially Expressed Genes 124 Class Prediction and Prognostic Prediction 125 ROC Curves for Evaluating Predictors and Differential Expression 126 Caveats and Admonitions 127 Final Note: Source Code Should be Available Acknowledgements References 13 A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larranaga, Inaki Inza and Jose L Flores) 131 Introduction 132 Genetic Networks 133 Probabilistic Graphical Models 134 Inferring Genetic Networks by Means of Probabilistic Graphical Models 135 Conclusions Acknowledgements References 14 Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen) 141 Introduction 142 Structure Prediction 143 Classifications of Structures 144 Comparing Protein Structures 145 Methods for the Discovery of Structure Motifs 146 Discussion and Conclusions References Index

64 citations

Journal ArticleDOI
TL;DR: A principled maximum-likelihood method for inferring community structure is given and how the results can be used to make improved estimates of the true structure of the network is demonstrated.
Abstract: In the study of networked systems such as biological, technological, and social networks the available data are often uncertain Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding We also give an example application to the detection of communities in a protein-protein interaction network

64 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163