Statistics for Biological Networks
How to Infer Networks from Data
(Sprache: Englisch)
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...
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Produktinformationen zu „Statistics for Biological Networks “
Klappentext zu „Statistics for Biological Networks “
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.
Inhaltsverzeichnis zu „Statistics for Biological Networks “
Introduction. From Clusters to Networks. Visualizing Networks. Inferring Network Topology. Network Identification. Static Network Models. Dynamic Network Models. Inference with Networks.
Autoren-Porträt von Ernst Wit, Veronica Vinciotti, Vilda Purutcuoglu
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.
Bibliographische Angaben
- Autoren: Ernst Wit , Veronica Vinciotti , Vilda Purutcuoglu
- 2026, 320 Seiten, 120 Abbildungen, Maße: 15,6 x 23,4 cm, Gebunden, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1439841470
- ISBN-13: 9781439841471
- Erscheinungsdatum: 26.01.2026
Sprache:
Englisch
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