By: Ernst Wit(Author), Veronica Vinciotti(Author), Vilda Purutcuoglu(Author)
An introduction to a new paradigm in social, technological, and scientific discourse, Statistics for Biological Networks: How to Infer Networks from Data 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
There are currently no reviews for this product. Be the first to review this product!
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.
Your orders support book donation projects
Fantastic service at a great price – I'll definitely use you again.
Search and browse over 110,000 wildlife and science products
Multi-currency. Secure worldwide shipping
Wildlife, science and conservation since 1985