Statistics for Biological Networks
How to Infer Networks from Data
Chapman and Hall/CRC – 2016 – 320 pages
An introduction to a new paradigm in social, technological, and scientific discourse, this book presents an overview of statistical methods for describing, modeling, and inferring biological networks using genomic and other types of data. It covers a large variety of modern statistical techniques, such as sparse graphical models, state space models, Boolean networks, and hidden Markov models. The authors address gene transcription data, microRNAs, ChIP-chip, and RNAi data. Along with end-of-chapter exercises, the text includes many real-world examples with implementations using a dedicated R package.
From clusters to networks
Inferring network topology
Static network models
Boolean network models
Dynamic network models
Single cell dynamics
State space modeling
Dynamic graphical models
Differential equation models
Inference with networks
Networks as explanatory variables
An expert in the field of statistical bioinformatics, Ernst Wit is a professor of statistics and probability at the University of Groningen.
Veronica Vinciotti is a lecturer in statistics at Brunel University.
Vilda Purutcuoglu is an instructor in statistics at Middle East Technical University.