Computational Network Analysis with R

Matthias Dehmer , Yongtang Shi , Frank Emmert-Streib 지음

Wiley-VCH 2016년 07월 22일 출간

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This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

목차

List of Contributors XV 1 Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks 1Atsushi Fukushima and Kozo Nishida 1.1 Introduction 1 1.1.1 An Introduction to Omics and Systems Biology 1 1.1.2 Correlation Networks in Omics and Systems Biology 1 1.1.3 Network Modules and Differential Network Approaches 2 1.1.4 Aims of this Chapter 4 1.2 What is DiffCorr? 4 1.2.1 Background 4 1.2.2 Methods 5 1.2.3 Main Functions in DiffCorr 5 1.2.4 Installing the DiffCorr Package 6 1.3 Constructing Co-Expression (Correlation) Networks from Omics Data Transcriptome Data set 8 1.3.1 Downloading the Transcriptome Data set 8 1.3.2 Data Filtering 9 1.3.3 Calculation of the Correlation and Visualization of Correlation Networks 11 1.3.4 Graph Clustering 15 1.3.5 Gene Ontology Enrichment Analysis 17 1.4 Differential Correlation Analysis by DiffCorr Package 21 1.4.1 Calculation of Differential Co-Expression between Organs in Arabidopsis 21 1.4.2 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis 26 1.4.3 Avoiding Pitfalls in (Differential) Correlation Analysis 29 1.5 Conclusion 30 Acknowledgments 30 Conflicts of Interest 30 References 30 2 Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs 35Benjamin A. Miller, Nicholas Arcolano, Stephen Kelley, and Nadya T. Bliss 2.1 Introduction 35 2.2 Chapter Definitions and Notation 36 2.3 Anomaly Detection in Graph Data 37 2.3.1 Neighborhood-Based Techniques 37 2.3.2 Frequent Subgraph Techniques 38 2.3.3 Anomalies in Random Graphs 39 2.4 Random Graph Models 41 2.4.1 Models with Attributes 41 2.4.2 Dynamic Graph Models 43 2.5 Spectral Subgraph Detection in Dynamic, Attributed Grap

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저자(글) Matthias Dehmer

Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also holds a position at the Universit t der Bundeswehr M nchen. His research interests are in applied mathematics, bioinformatics, systems biology, graph theory, complexity and information theory. He has written over 180 publications in his research areas. Yongtang Shi studied mathematics at Northwest University (Xi'an, China) and received his Ph.D in applied mathematics from Nankai University (Tianjin, China). He visited Technische Universit t Bergakademie Freiberg (Germany), UMIT (Austria) and Simon Fraser University (Canada). Currently, he is an associate professor at the Center for Combinatorics of Nankai University. His research interests are in graph theory and its applications, especially the applications of graph theory in mathematical chemistry, computer science and information theory. He has written over 40 publications in graph theory and its applications. Frank Emmert-Streib studied physics at the University of Siegen (Germany) gaining his PhD in theoretical physics from the University of Bremen (Germany). He received postdoctoral training from the Stowers Institute for Medical Re- search (Kansas City, USA) and the University of Washington (Seattle, USA). Currently, he is an associate professor at the Queen's University Belfast (UK) at the Center for Cancer Research and Cell Biology heading the Computational Biology and Machine Learning Laboratory. His main research interests are in the field of computational medicine, network biology and statistical

저자(글) Yongtang Shi

저자(글) Frank Emmert-Streib