Bayesian Networks in R

with Applications in Systems Biology

  • Radhakrishnan Nagarajan
  • Marco Scutari
  • Sophie Lèbre

Part of the Use R! book series (USE R, volume 48)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
    Pages 1-12
  3. Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
    Pages 13-58
  4. Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
    Pages 59-83
  5. Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
    Pages 85-101
  6. Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
    Pages 103-123
  7. Back Matter
    Pages 125-157

About this book

Introduction

Bayesian Networks in R with Applications in Systems Biology introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is gradually increased across the chapters with exercises and solutions for enhanced understanding and hands-on experimentation of key concepts. Applications focus on systems biology with emphasis on modeling pathways and signaling mechanisms from high throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regards as exemplified by their ability to discover new associations while validating known ones. It is also expected that the prevalence of publicly available high-throughput biological and healthcare data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

Keywords

Bayes Bayesian Theory Graph Theory Modeling R Systems Biology

Authors and affiliations

  • Radhakrishnan Nagarajan
    • 1
  • Marco Scutari
    • 2
  • Sophie Lèbre
    • 3
  1. 1.Division of Biomedical Informatics, Department of BiostatisticsUniversity of KentuckyLexingtonUSA
  2. 2., Genetics InstituteUniversity College LondonLondonUnited Kingdom
  3. 3., IcubeUniversité de StrasbourgStrasbourgFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-6446-4
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-6445-7
  • Online ISBN 978-1-4614-6446-4
  • About this book