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Bayesian Networks in R: With Applications in Systems Biology (Use R!) Paperback – 27 Apr 2013


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“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)

From the Back Cover

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.

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5.0 out of 5 starsExcellent resource for the bnlearn package
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