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