Catalogue


Protein interaction networks : computational analysis /
Aidong Zhang.
imprint
Cambridge ; New York : Cambridge University Press, 2009.
description
xiv, 278 p., [8] p. of plates : ill. (some col.) ; 26 cm.
ISBN
0521888956 (hardback : alk. paper), 9780521888950 (hardback : alk. paper)
format(s)
Book
Holdings
More Details
imprint
Cambridge ; New York : Cambridge University Press, 2009.
isbn
0521888956 (hardback : alk. paper)
9780521888950 (hardback : alk. paper)
contents note
Experimental approaches to generation of PPI data -- Computational methods for the prediction of PPIs -- Basic properties and measurements of protein interaction networks -- Modularity analysis of protein interaction networks -- Topological analysis of protein interaction networks / with Woo-chang Hwang -- Distance-based modularity analysis -- Graph-theoretic approaches to modularity analysis -- Flow-based analysis of protein interaction networks -- Statistics and machine learning based analysis of protein interaction networks / with Pritam Chanda and Lei Shi -- Integration of GO into the analysis of protein interaction networks / with Young-rae Cho -- Data fusion in the analysis of protein interaction networks.
catalogue key
6930127
 
Includes bibliographical references (p. 255-271) and index.
A Look Inside
Reviews
Review Quotes
"Up-to-date on the research and thoroughly comprehensive in coverage, Aidong Zhang's Protein Interaction Networks: Computational Analysis is an invaluable contribution to our understanding and knowledge of current analytic methods for protein interaction networks. Written with technical depth and sophistication, and replete with examples, this book will be both an indispensable manual for practitioners and a crucial textbook for teaching." Jiawei Han, Professor of Computer Science, University of Illinois at Urbana-Champaign
"Up-to-date on the research and thoroughly comprehensive in coverage, Aidong Zhang's Protein Interaction Networks: Computational Analysis is an invaluable contribution to our understanding and knowledge of current analytic methods for protein interaction networks. Written with technical depth and sophistication, and replete with examples, this book will be both an indispensable manual for practitioners and a crucial textbook for teaching."
"This book provides the most comprehensive and systematic review to an important biomedical research topic (protein interaction network). It gives its readers an opportunity to easily learn about this challenging topic and to begin investigating how they may contribute to it. Its great value makes it suitable for a broad range of readers, from students to experienced researchers."
"This book provides the most comprehensive and systematic review to an important biomedical research topic (protein interaction network). It gives its readers an opportunity to easily learn about this challenging topic and to begin investigating how they may contribute to it. Its great value makes it suitable for a broad range of readers, from students to experienced researchers." Dong Xu, Professor and Chair of the Computer Science Department, University of Missouri, Columbia
"This book provides a comprehensive coverage of current research issues and solutions in protein interaction networks. Within this new and exciting area of research, certain topics are explored in depth, including newest results reported by the author and other leading experts. I highly recommend this book for researchers and students who are interested in bioinformatics."
"This book provides a comprehensive coverage of current research issues and solutions in protein interaction networks. Within this new and exciting area of research, certain topics are explored in depth, including newest results reported by the author and other leading experts. I highly recommend this book for researchers and students who are interested in bioinformatics." Yi Pan, Chair and Professor of Computer Science, Georgia State University
"This book is a comprehensive and an excellent introduction to network biology in general and proteins networks in particular. It provides detailed description of the major computational concepts and their applications in systems biology. It is a must have book for anyone interested in this exciting topic." Mohammed J. Zaki, Professor of Computer Science, Rensselaer Polytechnic Institute
"This book is a comprehensive and an excellent introduction to network biology in general and proteins networks in particular. It provides detailed description of the major computational concepts and their applications in systems biology. It is a must have book for anyone interested in this exciting topic."
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Description for Bookstore
This comprehensive treatment of the computational methods available for the analysis of protein-protein interaction networks offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.
Bowker Data Service Summary
The first comprehensive survey of statistical, topological, data-mining, and ontology-based methods for analysing protein-protein interaction networks.
Main Description
The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, processes, and functions. Recent large-scale investigations of protein-protein interactions using such techniques as two-hybrid systems, mass spectrometry, and protein microarrays have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. The resulting large volume of protein-protein interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of protein-protein interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.
Main Description
The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, processes, and functions. Proteins seldom act as single isolated species; rather, proteins involved in the same cellular processes often interact with each other. Functions of uncharacterized proteins can be predicted through comparison with the interactions of similar known proteins. Recent large-scale investigations of protein-protein interactions using such techniques as two-hybrid systems, mass spectrometry, and protein microarrays have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. The resulting large volume of protein-protein interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of protein-protein interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.
Description for Bookstore
This is the first full survey of statistical, topological, data-mining, and ontology-based methods for analyzing protein-protein interaction networks. For each approach, the book explains the fundamental principles and discusses the benefits and drawbacks. The book then goes on to offer ideas for future research.
Table of Contents
Prefacep. xiii
Introductionp. 1
Rapid Growth of Protein-Protein Interaction Datap. 1
Computational Analysis of PPI Networksp. 3
Topological Features of PPI Networksp. 4
Modularity Analysisp. 5
Prediction of Protein Functions in PPI Networksp. 6
Integration of Domain Knowledgep. 7
Significant Applicationsp. 7
Organization of this Bookp. 9
Summaryp. 10
Experimental Approaches to Generation of PPI Datap. 11
Introductionp. 11
The Y2H Systemp. 11
Mass Spectrometry (MS) Approachesp. 13
Protein Microarraysp. 15
Public PPI Data and Their Reliabilityp. 15
Experimental PPI Data Setsp. 15
Public PPI Databasesp. 16
Functional Analysis of PPI Datap. 17
Summaryp. 20
Computational Methods for the Prediction of PPIsp. 21
Introductionp. 21
Genome-Scale Approachesp. 21
Sequence-Based Approachesp. 25
Structure-Based Approachesp. 26
Learning-Based Approachesp. 27
Network Topology-Based Approachesp. 29
Summaryp. 32
Basic Properties and Measurements of Protein Interaction Networksp. 33
Introductionp. 33
Representation of PPI Networksp. 33
Basic Conceptsp. 34
Basic Centralitiesp. 35
Degree Centralityp. 35
Distance-Based Centralitiesp. 35
Current-Flow-Based Centralityp. 37
Random-Walk-Based Centralityp. 40
Feedback-Based Centralityp. 41
Characteristics of PPI Networksp. 44
Summaryp. 49
Modularity Analysis of Protein Interaction Networksp. 50
Introductionp. 50
Useful Metrics for Modular Networksp. 51
Cliquesp. 51
Coresp. 51
Degree-Based Indexp. 52
Distance (Shortest Paths)-Based Indexp. 53
Methods for Clustering Analysis of Protein Interaction Networksp. 53
Traditional Clustering Methodsp. 54
Nontraditional Clustering Methodsp. 55
Validation of Modularityp. 56
Clustering Coefficientp. 56
Validation Based on Agreement with Annotated Protein Function Databasesp. 57
Validation Based on the Definition of Clusteringp. 59
Topological Validationp. 60
Supervised Validationp. 61
Statistical Validationp. 61
Validation of Protein Function Predictionp. 62
Summaryp. 63
Topological Analysis of Protein Interaction Networksp. 63
Introductionp. 63
Overview and Analysis of Essential Network Componentsp. 64
Error and Attack Tolerance of Complex Networksp. 64
Role of High-Degree Nodes in Biological Networksp. 67
Betweenness, Connectivity, and Centralityp. 69
Bridging Centrality Measurementsp. 73
Performance of Bridging Centrality with Synthetic and Real-World Networksp. 75
Assessing Network Disruption, Structural Integrity, and Modularityp. 77
Network Modularization Using the Bridge Cut Algorithmp. 84
Use of Bridging Nodes in Drug Discoveryp. 87
Biological Correlates of Bridging Centralityp. 88
Results from Drug Discovery-Relevant Human Networksp. 92
Comparison to Alternative Approaches: Yeast Cell Cycle State Space Networkp. 94
Potential of Bridging Centrality as a Drug Discovery Toolp. 95
PathRatio: A Novel Topological Method for Predicting Protein Functionsp. 97
Weighted PPI Networkp. 97
Protein Connectivity and Interaction Reliabilityp. 98
PathStrength and PathRatio Measurementsp. 99
Analysis of the PathRatio Topological Measurementp. 100
Experimental Resultsp. 101
Summaryp. 108
Distance-Based Modularity Analysisp. 109
Introductionp. 109
Topological Distance Measurement Based on Coefficientsp. 109
Distance Measurement by Network Distancep. 112
PathRadio Methodp. 112
Averaging the Distancesp. 113
Ensemble Methodp. 114
Similarity Metricsp. 115
Base Algorithmsp. 116
Consensus Methodsp. 116
Results of the Ensemble Methodsp. 118
UVclusterp. 118
Similarity Learning Methodp. 120
Measurement of Biological Distancep. 124
Sequence Similarity-Based Measurementsp. 124
Structural Similarity-Based Measurementsp. 125
Gene Expression Similarity-Based Measurementsp. 127
Summaryp. 128
Graph-Theoretic Approaches to Modularity Analysisp. 130
Introductionp. 130
Finding Dense Subgraphsp. 130
Enumeration of Complete Subgraphsp. 130
Monte Carlo Optimizationp. 131
Molecular Complex Detectionp. 132
Clique Percolationp. 133
Merging by Statistical Significancep. 134
Super-Paramagnetic Clusteringp. 136
Finding the Best Partitionp. 137
Recursive Minimum Cutp. 137
Restricted Neighborhood Search Clustering (RNSC)p. 138
Betweenness Cutp. 140
Markov Clusteringp. 140
Line Graph Generationp. 143
Graph Reduction-Based Approachp. 144
Graph Reductionp. 144
Hierarchical Modularizationp. 146
Time Complexityp. 147
k Effects on Graph Reductionp. 147
Hierarchical Structure of Modulesp. 149
Summaryp. 150
Flow-Based Analysis of Protein Interaction Networksp. 152
Introductionp. 152
Protein Function Prediction Using the FunctionalFlow Algorithmp. 153
CASCADE: A Dynamic Flow Simulation for Modularity Analysisp. 155
Occurrence Probability and Related Modelsp. 156
The CASCADE Algorithmp. 158
Analysis of Prototypical Datap. 160
Significance of Individual Clustersp. 162
Analysis of Functional Annotationp. 164
Comparative Assessment of CASCADE with Other Approachesp. 169
Analysis of Robustnessp. 175
Analysis of Computational Complexityp. 175
Advantages of the CASCADE Methodp. 176
Functional Flow Analysis in Weighted PPI Networksp. 177
Functional Influence Modelp. 178
Functional Flow Simulation Algorithmp. 179
Time Complexity of Flow Simulationp. 180
Detection of Overlapping Modulesp. 181
Detection of Disjoint Modulesp. 189
Functional Flow Pattern Miningp. 191
Summaryp. 198
Statistics and Machine Learning Based Analysis of Protein Interaction Networksp. 199
Introductionp. 199
Applications of Markov Random Field and Belief Propagation for Protein Function Predictionp. 200
Protein Function Prediction Using Kernel-Based Statistical Learning Methodsp. 207
Protein Function Prediction Using Bayesian Networksp. 211
Improving Protein Function Prediction Using Bayesian Integrative Methodsp. 213
Summaryp. 214
Integration of GO into the Analysis of Protein Interaction Networksp. 216
Introductionp. 216
GO structurep. 217
GO Annotationsp. 217
Semantic Similarity-Based Integrationp. 218
Structure-Based Methodsp. 219
Information Content-Based Methodsp. 220
Combination of Structure and Information Contentp. 221
Semantic Interactivity-Based Integrationp. 223
Estimate of Interaction Reliabilityp. 223
Functional Co-Occurrencep. 224
Topological Significancep. 225
Protein Lethalityp. 226
Functional Module Detectionp. 227
Statistical Assessmentp. 227
Supervised Validationp. 229
Probabilistic Approaches for Function Predictionp. 231
GO Index-Based Probabilistic Methodp. 231
Semantic Similarity-Based Probabilistic Methodp. 235
Summaryp. 241
Data Fusion in the Analysis of Protein Interaction Networksp. 243
Introductionp. 243
Integration of Gene Expression with PPI Networksp. 243
Integration of Protein Domain Information with PPI Networksp. 244
Integration of Protein Localization Information with PPI Networksp. 245
Integration of Several Data Sources with PPI Networksp. 247
Kernel-Based Methodsp. 247
Bayesian Model-Based Methodp. 249
Summaryp. 249
Conclusionp. 251
Bibliographyp. 255
Indexp. 273
Table of Contents provided by Ingram. All Rights Reserved.

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