The interdisciplinary nature of bioinformatics presents a challenge in integrating concepts, methods, software, and multi-platform data. Although there have been rapid developments in new technology and an inundation of statistical methodology and software for the analysis of microarray gene expression arrays, there exist few rigorous statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book 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.

Bayesian Inference for Gene Expression and Proteomics PDF

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