Preface | p. xiii |
Introduction | p. 1 |
Rapid Growth of Protein-Protein Interaction Data | p. 1 |
Computational Analysis of PPI Networks | p. 3 |
Topological Features of PPI Networks | p. 4 |
Modularity Analysis | p. 5 |
Prediction of Protein Functions in PPI Networks | p. 6 |
Integration of Domain Knowledge | p. 7 |
Significant Applications | p. 7 |
Organization of this Book | p. 9 |
Summary | p. 10 |
Experimental Approaches to Generation of PPI Data | p. 11 |
Introduction | p. 11 |
The Y2H System | p. 11 |
Mass Spectrometry (MS) Approaches | p. 13 |
Protein Microarrays | p. 15 |
Public PPI Data and Their Reliability | p. 15 |
Experimental PPI Data Sets | p. 15 |
Public PPI Databases | p. 16 |
Functional Analysis of PPI Data | p. 17 |
Summary | p. 20 |
Computational Methods for the Prediction of PPIs | p. 21 |
Introduction | p. 21 |
Genome-Scale Approaches | p. 21 |
Sequence-Based Approaches | p. 25 |
Structure-Based Approaches | p. 26 |
Learning-Based Approaches | p. 27 |
Network Topology-Based Approaches | p. 29 |
Summary | p. 32 |
Basic Properties and Measurements of Protein Interaction Networks | p. 33 |
Introduction | p. 33 |
Representation of PPI Networks | p. 33 |
Basic Concepts | p. 34 |
Basic Centralities | p. 35 |
Degree Centrality | p. 35 |
Distance-Based Centralities | p. 35 |
Current-Flow-Based Centrality | p. 37 |
Random-Walk-Based Centrality | p. 40 |
Feedback-Based Centrality | p. 41 |
Characteristics of PPI Networks | p. 44 |
Summary | p. 49 |
Modularity Analysis of Protein Interaction Networks | p. 50 |
Introduction | p. 50 |
Useful Metrics for Modular Networks | p. 51 |
Cliques | p. 51 |
Cores | p. 51 |
Degree-Based Index | p. 52 |
Distance (Shortest Paths)-Based Index | p. 53 |
Methods for Clustering Analysis of Protein Interaction Networks | p. 53 |
Traditional Clustering Methods | p. 54 |
Nontraditional Clustering Methods | p. 55 |
Validation of Modularity | p. 56 |
Clustering Coefficient | p. 56 |
Validation Based on Agreement with Annotated Protein Function Databases | p. 57 |
Validation Based on the Definition of Clustering | p. 59 |
Topological Validation | p. 60 |
Supervised Validation | p. 61 |
Statistical Validation | p. 61 |
Validation of Protein Function Prediction | p. 62 |
Summary | p. 63 |
Topological Analysis of Protein Interaction Networks | p. 63 |
Introduction | p. 63 |
Overview and Analysis of Essential Network Components | p. 64 |
Error and Attack Tolerance of Complex Networks | p. 64 |
Role of High-Degree Nodes in Biological Networks | p. 67 |
Betweenness, Connectivity, and Centrality | p. 69 |
Bridging Centrality Measurements | p. 73 |
Performance of Bridging Centrality with Synthetic and Real-World Networks | p. 75 |
Assessing Network Disruption, Structural Integrity, and Modularity | p. 77 |
Network Modularization Using the Bridge Cut Algorithm | p. 84 |
Use of Bridging Nodes in Drug Discovery | p. 87 |
Biological Correlates of Bridging Centrality | p. 88 |
Results from Drug Discovery-Relevant Human Networks | p. 92 |
Comparison to Alternative Approaches: Yeast Cell Cycle State Space Network | p. 94 |
Potential of Bridging Centrality as a Drug Discovery Tool | p. 95 |
PathRatio: A Novel Topological Method for Predicting Protein Functions | p. 97 |
Weighted PPI Network | p. 97 |
Protein Connectivity and Interaction Reliability | p. 98 |
PathStrength and PathRatio Measurements | p. 99 |
Analysis of the PathRatio Topological Measurement | p. 100 |
Experimental Results | p. 101 |
Summary | p. 108 |
Distance-Based Modularity Analysis | p. 109 |
Introduction | p. 109 |
Topological Distance Measurement Based on Coefficients | p. 109 |
Distance Measurement by Network Distance | p. 112 |
PathRadio Method | p. 112 |
Averaging the Distances | p. 113 |
Ensemble Method | p. 114 |
Similarity Metrics | p. 115 |
Base Algorithms | p. 116 |
Consensus Methods | p. 116 |
Results of the Ensemble Methods | p. 118 |
UVcluster | p. 118 |
Similarity Learning Method | p. 120 |
Measurement of Biological Distance | p. 124 |
Sequence Similarity-Based Measurements | p. 124 |
Structural Similarity-Based Measurements | p. 125 |
Gene Expression Similarity-Based Measurements | p. 127 |
Summary | p. 128 |
Graph-Theoretic Approaches to Modularity Analysis | p. 130 |
Introduction | p. 130 |
Finding Dense Subgraphs | p. 130 |
Enumeration of Complete Subgraphs | p. 130 |
Monte Carlo Optimization | p. 131 |
Molecular Complex Detection | p. 132 |
Clique Percolation | p. 133 |
Merging by Statistical Significance | p. 134 |
Super-Paramagnetic Clustering | p. 136 |
Finding the Best Partition | p. 137 |
Recursive Minimum Cut | p. 137 |
Restricted Neighborhood Search Clustering (RNSC) | p. 138 |
Betweenness Cut | p. 140 |
Markov Clustering | p. 140 |
Line Graph Generation | p. 143 |
Graph Reduction-Based Approach | p. 144 |
Graph Reduction | p. 144 |
Hierarchical Modularization | p. 146 |
Time Complexity | p. 147 |
k Effects on Graph Reduction | p. 147 |
Hierarchical Structure of Modules | p. 149 |
Summary | p. 150 |
Flow-Based Analysis of Protein Interaction Networks | p. 152 |
Introduction | p. 152 |
Protein Function Prediction Using the FunctionalFlow Algorithm | p. 153 |
CASCADE: A Dynamic Flow Simulation for Modularity Analysis | p. 155 |
Occurrence Probability and Related Models | p. 156 |
The CASCADE Algorithm | p. 158 |
Analysis of Prototypical Data | p. 160 |
Significance of Individual Clusters | p. 162 |
Analysis of Functional Annotation | p. 164 |
Comparative Assessment of CASCADE with Other Approaches | p. 169 |
Analysis of Robustness | p. 175 |
Analysis of Computational Complexity | p. 175 |
Advantages of the CASCADE Method | p. 176 |
Functional Flow Analysis in Weighted PPI Networks | p. 177 |
Functional Influence Model | p. 178 |
Functional Flow Simulation Algorithm | p. 179 |
Time Complexity of Flow Simulation | p. 180 |
Detection of Overlapping Modules | p. 181 |
Detection of Disjoint Modules | p. 189 |
Functional Flow Pattern Mining | p. 191 |
Summary | p. 198 |
Statistics and Machine Learning Based Analysis of Protein Interaction Networks | p. 199 |
Introduction | p. 199 |
Applications of Markov Random Field and Belief Propagation for Protein Function Prediction | p. 200 |
Protein Function Prediction Using Kernel-Based Statistical Learning Methods | p. 207 |
Protein Function Prediction Using Bayesian Networks | p. 211 |
Improving Protein Function Prediction Using Bayesian Integrative Methods | p. 213 |
Summary | p. 214 |
Integration of GO into the Analysis of Protein Interaction Networks | p. 216 |
Introduction | p. 216 |
GO structure | p. 217 |
GO Annotations | p. 217 |
Semantic Similarity-Based Integration | p. 218 |
Structure-Based Methods | p. 219 |
Information Content-Based Methods | p. 220 |
Combination of Structure and Information Content | p. 221 |
Semantic Interactivity-Based Integration | p. 223 |
Estimate of Interaction Reliability | p. 223 |
Functional Co-Occurrence | p. 224 |
Topological Significance | p. 225 |
Protein Lethality | p. 226 |
Functional Module Detection | p. 227 |
Statistical Assessment | p. 227 |
Supervised Validation | p. 229 |
Probabilistic Approaches for Function Prediction | p. 231 |
GO Index-Based Probabilistic Method | p. 231 |
Semantic Similarity-Based Probabilistic Method | p. 235 |
Summary | p. 241 |
Data Fusion in the Analysis of Protein Interaction Networks | p. 243 |
Introduction | p. 243 |
Integration of Gene Expression with PPI Networks | p. 243 |
Integration of Protein Domain Information with PPI Networks | p. 244 |
Integration of Protein Localization Information with PPI Networks | p. 245 |
Integration of Several Data Sources with PPI Networks | p. 247 |
Kernel-Based Methods | p. 247 |
Bayesian Model-Based Method | p. 249 |
Summary | p. 249 |
Conclusion | p. 251 |
Bibliography | p. 255 |
Index | p. 273 |
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