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.

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Yes, you can access Computational Network Analysis with R by Matthias Dehmer, Yongtang Shi, Frank Emmert-Streib, Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib in PDF and/or ePUB format, as well as other popular books in Medicine & Biostatistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Year

Print ISBN

eBook ISBN

Edition

Topic

Medicine

Subtopic

Biostatistics

Cover
Title Page
Copyright
Table of Contents
List of Contributors
Chapter 1: Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks
Chapter 2: Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs
Chapter 3: Bayesian Computational Algorithms for Social Network Analysis
Chapter 4: Threshold Degradation in R Using iDEMO
Chapter 5: Optimization of Stratified Sampling with the R Package SamplingStrata: Applications to Network Data
Chapter 6: Exploring the Role of Small Molecules in Biological Systems Using Network Approaches
Chapter 7: Performing Network Alignments with R
Chapter 8: ℓ1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields
Chapter 9: Cluster Analysis of Social Networks Using R
Chapter 10: Inference and Analysis of Gene Regulatory Networks in R
Chapter 11: Visualization of Biological Networks Using NetBioV
Index
End User License Agreement

About this book

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Table of contents