Bayesian inference for gene expression and proteomics
By: edited by Kim-Anh Do, Peter Müller, Marina Vannucci
Contributors: Do, Kim-Anh, 1960-; Müller, Peter, 1963 Aug. 9-; Vannucci, Marina, 1966-
Publisher: Cambridge University Press
Published: 2006
Place of publication: Cambridge; New York
Language: eng
Number of pages: 437
Subjects: Gene expression -- Statistical methods; Proteomics -- Statistical methods
Description: Discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data, from medical research and molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical models. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools, and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.
Notes: Includes bibliographical references