Bayesian networks in R, with applications in systems biology
Nagarajan, Radhakrishnan
Bayesian networks in R : with applications in systems biology [Documento electrónico] / Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre. - New York, NY : Springer , 2013 . - XIII, 157 p. : il.. - (Use R) 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.
Clicar aqui para aceder a um recurso externo
ISBN 978-1-4614-6446-4
Teoria da decisão estatística bayesiana
R (Linguagem de programação)
LCC QA279.5
Bayesian networks in R : with applications in systems biology [Documento electrónico] / Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre. - New York, NY : Springer , 2013 . - XIII, 157 p. : il.. - (Use R) 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.
Clicar aqui para aceder a um recurso externo
ISBN 978-1-4614-6446-4
Teoria da decisão estatística bayesiana
R (Linguagem de programação)
LCC QA279.5
