Use R!

Bayesian Networks in R

with Applications in Systems Biology

Authors: Nagarajan, Radhakrishnan, Scutari, Marco, Lèbre, Sophie

  • Represents a unique combination of introduction to concepts and examples from open-source R software
  • Each chapter is accompanied by examples and exercises with solutions for enhanced understanding and experimentation
  • Useful for students and researchers across many disciplines
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Buy this book

eBook 35,69 €
price for Spain (gross)
  • ISBN 978-1-4614-6446-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 44,71 €
price for Spain (gross)
  • ISBN 978-1-4614-6445-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Rent the eBook  
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About this book

Bayesian Networks in R with Applications in Systems Biology is unique as it 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 also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses 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 regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

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.

 

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)


Table of contents (5 chapters)

  • Introduction

    Nagarajan, Radhakrishnan (et al.)

    Pages 1-12

    Preview Buy Chapter 30,19 €
  • Bayesian Networks in the Absence of Temporal Information

    Nagarajan, Radhakrishnan (et al.)

    Pages 13-58

    Preview Buy Chapter 30,19 €
  • Bayesian Networks in the Presence of Temporal Information

    Nagarajan, Radhakrishnan (et al.)

    Pages 59-83

    Preview Buy Chapter 30,19 €
  • Bayesian Network Inference Algorithms

    Nagarajan, Radhakrishnan (et al.)

    Pages 85-101

    Preview Buy Chapter 30,19 €
  • Parallel Computing for Bayesian Networks

    Nagarajan, Radhakrishnan (et al.)

    Pages 103-123

    Preview Buy Chapter 30,19 €

Buy this book

eBook 35,69 €
price for Spain (gross)
  • ISBN 978-1-4614-6446-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 44,71 €
price for Spain (gross)
  • ISBN 978-1-4614-6445-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Bayesian Networks in R
Book Subtitle
with Applications in Systems Biology
Authors
Series Title
Use R!
Series Volume
48
Copyright
2013
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4614-6446-4
DOI
10.1007/978-1-4614-6446-4
Softcover ISBN
978-1-4614-6445-7
Series ISSN
2197-5736
Edition Number
1
Number of Pages
XIII, 157
Number of Illustrations and Tables
36 b/w illustrations
Topics