Bayesian Modeling in Bioinformatics

About this book

This title discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis.

Contents

General Overview. Unsupervised Methods: Clustering. Supervised Methods: Classification. Gene Selection. Proteomics and Protein Structure Prediction. Metabonomics. Genomics. Gene Network and Pathway Analysis. Genetics.

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Biography

Dipak K. Dey is a professor and head of the Department of Statistics at the University of Connecticut. Samiran Ghosh is an assistant professor in the Department of Mathematical Sciences at Indiana University-Purdue University. Bani K. Mallick is a professor of statistics and director of the Bayesian Bioinformatics Laboratory at Texas A&M University.