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 theapproaches presented in the book.
| ISBN: | 9781461464457 |
| Publication date: | 27th April 2013 |
| Author: | Radhakrishnan Nagarajan, Marco Scutari, Sophie Lßèbre |
| Publisher: | Springer an imprint of Springer New York |
| Format: | Paperback |
| Pagination: | 157 pages |
| Series: | Use R! |
| Genres: |
Probability and statistics Mathematical and statistical software Compilers and interpreters |
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.
Bayesian Networks in R features in the following genres: Probability and statistics, Mathematical and statistical software, Compilers and interpreters
Paperback. Not Available.
Bayesian Networks in R was written by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lßèbre and published by Springer an imprint of Springer New York
Bayesian Networks in R has 157 pages
Yes it is part of Use R! series