Bayesian inference for gene expression and proteomics
by Kim-Anh Do, Peter Müller, and Marina Vannucci
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.
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Bayesian inference for gene expression and proteomics
2006, Cambridge University Press
in English
052186092X 9780521860925
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