Protein interaction networks : computational analysis / Aidong Zhang. — Cambridge : Cambridge University Press, 2009. – (58.17421/Z63)

Contents

    Contents
    Preface
    1 Introduction
    1.1 Rapid Growth of Protein-Protein Interaction Data
    1.2 Computational Analysis of PPI Networks
    1.3 Significant Applications
    1.4 Organization of this Book
    1.5 Summary
    2 Experimental Approaches to Generation of PPI Data
    2.1 Introduction
    2.2 The Y2H System
    2.3 Mass Spectrometry (MS) Approaches
    2.4 Protein Microarrays
    2.5 Public PPI Data and Their Reliability
    2.6 Summary
    3 Computational Methods for the Prediction of PPIs
    3.1 Introduction
    3.2 Genome-Scale Approaches
    3.3 Sequence-Based Approaches
    3.4 Structure-Based Approaches
    3.5 Learning-Based Approaches
    3.6 Network Topology-Based Approaches
    3.7 Summary
    4 Basic Properties and Measurements of Protein Interaction Networks
    4.1 Introduction
    4.2 Representation of PPI Networks
    4.3 Basic Concepts
    4.4 Basic Centralities
    4.5 Characteristics of PPI Networks
    4.6 Summary
    5 Modularity Analysis of Protein Interaction Networks
    5.1 Introduction
    5.2 Useful Metrics for Modular Networks
    5.3 Methods for Clustering Analysis of Protein Interaction Networks
    5.4 Validation of Modularity
    5.5 Summary
    6 Topological Analysis of Protein Interaction Networks
    6.1 Introduction
    6.2 Overview and Analysis of Essential Network Components
    6.3 Bridging Centrality Measurements
    6.4 Network Modularization Using the Bridge Cut Algorithm
    6.5 Use of Bridging Nodes in Drug Discovery
    6.6 PathRatio: A Novel Topological Method for Predicting Protein Functions
    6.7 Summary
    7 Distance-Based Modularity Analysis
    7.1 Introduction
    7.2 Topological Distance Measurement Based on Coefficients
    7.3 Distance Measurement by Network Distance
    7.4 Ensemble Method
    7.5 UVCLUSTER
    7.6 Similarity Learning Method
    7.7 Measurement of Biological Distance
    7.8 Summary
    8 Graph-Theoretic Approaches to Modularity Analysis
    8.1 Introduction
    8.2 Finding Dense Subgraphs
    8.3 Finding the Best Partition
    8.4 Graph Reduction-Based Approach
    8.5 Summary
    9 Flow-Based Analysis of Protein Interaction Networks
    9.1 Introduction
    9.2 Protein Function Prediction Using the FunctionalFlow Algorithm
    9.3 CASCADE: A Dynamic Flow Simulation for Modularity Analysis
    9.4 Functional Flow Analysis in Weighted PPI Networks
    9.5 Summary
    10 Statistics and Machine Learning Based Analysis of Protein Interaction Networks 199
    10.1 Introduction 199
    10.2 Applications of Markov Random Field and Belief Propagation for Protein Function Prediction 200
    10.3 Protein Function Prediction Using Kernel-Based Statistical Learning Methods
    10.4 Protein Function Prediction Using Bayesian Networks 211
    10.5 Improving Protein Function Prediction Using Bayesian Integrative Methods
    10.6 Summary
    11 Integration of GO into the Analysis of Protein Interaction Networks
    11.1 Introduction
    11.2 GO structure
    11.3 Semantic Similarity-Based Integration
    11.4 Semantic Interactivity-Based Integration
    11.5 Estimate of Interaction Reliability
    11.6 Functional Module Detection
    11.7 Probabilistic Approaches for Function Prediction
    11.8 Summary
    12 Data Fusion in the Analysis of Protein Interaction Networks
    12.1 Introduction
    12.2 Integration of Gene Expression with PPI Networks
    12.3 Integration of Protein Domain Information with PPI Networks
    12.4 Integration of Protein Localization Information with PPI Networks
    12.5 Integration of Several Data Sources with PPI Networks
    12.6 Summary
    13 Conclusion
    Bibliography
    Index
    Color plates follow page 82