Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection

Description

This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights. Preface Contents Part I: Comparative Network Analysis 1 Global Alignment of PPI Networks • Cesim Erten 2 Effective Random Walk Models for Comparative Network Analysis • Hyundoo Jeong 3 Computational Methods for Protein–Protein Interaction Network Alignment • Ruiquan Ge, Qing Wu, and Jinbo Xu 4 BioFabric Visualization of Network Alignments • Rishi M. Desai, William J. R. Longabaugh, and Wayne B. Hayes Part II: Network Module Detection 5 Motifs in Biological Networks • Rasha Elhesha, Aisharjya Sarkar, and Tamer Kahveci 6 Module Identification of Biological Networks via Graph Partition • Yijie Wang 7 Network Module Detection to Decipher Heterogeneity of Cancer Mutations • Yoo-Ah Kim Part III: Network-Based Omics Data Analysis 8 Integrated Network-Based Computational Analysis for Drug Development • Mijin Kwon, Soorin Yim, Gwangmin Kim, and Doheon Lee 9 Network Propagation for the Analysis of Multi-omics Data • Minwoo Pak, Dabin Jeong, Ji Hwan Moon, Hongryul Ann, Benjamin Hur, Sangseon Lee, and Sun Kim