Statistics for Biological Networks

How to Infer Networks from Data
1. Auflage 2026
ISBN: 978-1-4398-4147-1
Verlag: Taylor & Francis Inc

Buch, Englisch, 320 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Chapman & Hall/CRC Interdisciplinary Statistics

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.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Wit, Ernst

Vinciotti, Veronica

Purutcuoglu, Vilda

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik

Inhaltsverzeichnis

Introduction. From Clusters to Networks. Visualizing Networks. Inferring Network Topology. Network Identification. Static Network Models. Dynamic Network Models. Inference with Networks.

Über Autor(innen)

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.