Statistical and Machine Learning Approaches for Network Analysis

Contents

Cover

Title Page

Copyright

Dedication

Preface

Contributors

Chapter 1: A Survey of Computational Approaches to Reconstruct and Partition Biological Networks

1.1 Introduction

1.2 Biological Networks

1.3 Genome-wide Measurements

1.4 Reconstruction of Biological Networks

1.5 Partitioning Biological Networks

1.6 Discussion

References

Chapter 2: Introduction to Complex Networks: Measures, Statistical Properties, and Models

2.1 Introduction

2.2 Representation of Networks

2.3 Classical Network

2.4 Scale-Free Network

2.5 Small-World Network

2.6 Clustered Network

2.7 Hierarchical Modularity

2.8 Network Motif

2.9 Assortativity

2.10 Reciprocity

2.11 Weighted Networks

2.12 Network Complexity

2.13 Centrality

2.14 Conclusion

References

Chapter 3: Modeling for Evolving Biological Networks

3.1 Introduction

3.2 Unified Evolving Network Model: Reproduction of Heterogeneous Connectivity, Hierarchical Modularity, and Disassortativity

3.3 Modeling Without Parameter Tuning: A Case Study of Metabolic Networks

3.4 Bipartite Relationship: A Case Study of Metabolite Distribution

3.5 Conclusion

References

Chapter 4: Modularity Configurations in Biological Networks with Embedded Dynamics

4.1 Introduction

4.2 Methods

4.3 Results

4.4 Discussion and Concluding Remarks

Acknowledgment

Supporting Information

References

Chapter 5: Influence of Statistical Estimators on the Large-Scale Causal Inference ...