Nagarajan, Radhakrishnan, Scutari, Marco, Lèbre, Sophie
2013, XIII, 157 p. 36 illus.
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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
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.
Content Level »Research
Keywords »Bayes - Bayesian Theory - Graph Theory - Modeling - R - Systems Biology
Introduction.- Bayesian Networks in the Absence of Temporal Information.- Bayesian Networds in the Presence of Temporal Information.- Bayesian Network Inference Algorithms.- Parallel Computing for Bayesian Networks.- Solutions.- Index.- References.