Computational Network Analysis with R: Applications in Biology, Medicine and Chemistry

Matthias Dehmer (Editor), Yongtang Shi (Editor), Frank Emmert-Streib (Editor)



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.

Table of Contents

Differential correlation technique to analyze biological networks: DiffCorr

Challenges of computational network analysis with R

Software and practices for visualizing network data in biology and medicine

Efficient anomaly detection in dynamic, attributed graphs by using R

Chemical informatics functionality in R

Biological network comparison

Degradation analysis in R using uDEMO

Penalized methods in high-dimensional Gaussian graphical models