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© 2013

Bayesian Networks in R

with Applications in Systems Biology

Book

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

  1. 1.Division of Biomedical Informatics, Department of BiostatisticsUniversity of KentuckyLexingtonUSA
  2. 2., Genetics InstituteUniversity College LondonLondonUnited Kingdom
  3. 3., IcubeUniversité de StrasbourgStrasbourgFrance

About the authors

Radhakrishnan Nagarajan, Ph.D.

Dr. Nagarajan is an Associate Professor in the Division of Biomedical Informatics, Department of Biostatistics at the College of Public Health, University of Kentucky, Lexington, USA. His areas of research falls under evidence-based science that demands knowledge discovery from high-dimensional molecular and observational healthcare data sets using a combination of statistical algorithms, machine learning and network science approaches.

Contact: Division of Biomedical Informatics/Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, MDS 230F, Lexington, KY 40536-0082.

 

 Marco Scutari, Ph.D.

Dr. Scutari studied Statistics and Computer Science at the University of Padova, Italy. He earned his Ph.D. in Statistics in Padova under the guidance of Prof. A. Brogini, studying graphical model learning. He is now Research Associate at the Genetics Institute, University College London (UCL). His research focuses on the theoretical properties of Bayesian networks and their applications to biological data, and he is the author and maintainer of the bnlearn R package.

Contact: Genetics Institute, University College London Darwin Building, Room 212 London, WC1E 6BT United Kingdom.

 

 Sophie Lèbre, Ph.D.

Dr. Lèbre is a Lecturer in the Department of Computer Science at the University of Strasbourg, France.
She originally earned her Ph.D. in Applied Mathematics at the University of Evry-val-d'Essone (France) under the guidance of Prof. B. Prum. Her research focuses on graphical modeling and dynamic Bayesian network inference, devoted to recovering genetic interaction networks from post genomic data. She is the author and maintainer of the G1DBN and the ARTIVA R packages for dynamic Bayesian network inference.

Contact: LSIIT, Equipe BFO, Pôle API, Bd Sébastien Brant - BP 10413, F - 67412 Illkirch CEDEX, France.

Contact: Division of Biomedical Informatics/Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, MDS 230F, Lexington, KY 40536-0082.

 

Bibliographic information

Reviews

“This book is a readable mix of short explanations of Bayesian network principles and implementations in R. I think it is most useful for readers who already have intermediate exposure to both the principles and R implementations. … Each chapter has several exercises (answers are at the end of the book) and the book could be used as an introductory course text.” (Thomas Burr, Technometrics, Vol. 56 (3), August, 2014)