Statistics for Biological Networks
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Statistics for Biological Networks: How to Infer Networks from Data(Chapman & Hall/CRC Interdisciplinary Statistics)

Statistics for Biological Networks: How to Infer Networks from Data(Chapman & Hall/CRC Interdisciplinary Statistics)


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About the Book

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.

Table of Contents:
Introduction From clusters to networks Visualizing networks Inferring network topology Network evolution Network parameters Network identification Adjacency matrices Finding modules Finding pathways Finding (sub)networks Static network models Linear models Graphical 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 Survival analysis

About the Author :
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.


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Product Details
  • ISBN-13: 9781439841495
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: Chapman & Hall/CRC
  • Language: English
  • No of Pages: 320
  • Sub Title: How to Infer Networks from Data
  • ISBN-10: 1439841497
  • Publisher Date: 30 Jun 2019
  • Binding: Digital (delivered electronically)
  • No of Pages: 320
  • Series Title: Chapman & Hall/CRC Interdisciplinary Statistics


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Statistics for Biological Networks: How to Infer Networks from Data(Chapman & Hall/CRC Interdisciplinary Statistics)
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