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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Professional Practice & Development
Illustrationen
120 s/w Abbildungen
120 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
ISBN-13
978-1-4398-4147-1 (9781439841471)
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Schweitzer Klassifikation
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.
Autor*in
USI Universita della Svizzera italiana, Switzerland
Brunel University, Middlesex, UK
Middle East Technical University, Ankara, Turkey
Introduction. From Clusters to Networks. Visualizing Networks. Inferring Network Topology. Network Identification. Static Network Models. Dynamic Network Models. Inference with Networks.
