Bayesian Networks in R: with Applications in Systems Biology

by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lebre

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.

170 pages, Paperback

First published April 1, 2013

Reviews

[Eric Lawton · Rating 2 out of 5]

Status changed to "read", but I didn’t finish it, and won’t ever.
I think I wasted my money on this. I couldn't understand the maths parts at all until I got a more better book on the same topic, Causality by Judea Pearl. Actually, this book (BNinR) refers a lot to Pearl's earlier book which really defined the field, so the only real value of this book is that I got the reference to a much better book and that it does have a good set of examples in R.

After reading enough of Pearl to understand the first few theorems in BNinR, I then went deeper into BNinR and found the reason I couldn't understand it. The book backwards written is. I didn't understand the theorems because all the definitions are written after the theorem, so can't actually be understood until you've got to the end of the section, at which point you go back and it becomes clearer. But having bought Causality and another excellent book Risk Assessment and Decision Analysis with Bayesian Networks, there not much point to reading BNinR, except I'm working the R examples.

On a more general note, Bayesian Networks is proving to be what I hoped it would be, a really useful technique, supported by machine learning, for analyzing complex networks in ecology, economics, social science and systems generally.

[Changye Li · Rating 4 out of 5]

适合我这种有迫切需要上手的人