Estimation and testing in time-course microarray experiments / C. Angelini, D. De Canditiis, and M. Pensky -- Classification for differential gene expression using Bayesian hierarchical models / Natalia Bochkina and Alex Lewin -- Applications of MOSS for discrete multi-way data / Adrian Dobra [and others] -- Nonparametric Bayesian bioinformatics / David B. Dunson -- Measurement error and survival model for cDNA micro-arrays / Jonathan A.L. Gelfond and Joseph G. Ibrahim -- Bayesian robust inference for differential gene expression / Raphael Gottardo -- Bayesian hidden Markov modeling of array CGH data / Subharup Guha -- Bayesian approaches to phylogenetic analysis / Mark T. Holder, Jeet Sukumaran, and Rafe M. Brown -- Gene selection for the identification of biomarkers in high-throughout data / Jaesik Jeong [and others] -- Sparsity priors for protein-protein interaction predictions / Inyoung Kim, Yin Liu, and Hongyu Zhao -- Learning Bayesian networks for gene expression data / Farning Liang -- In-vitro to in-vivo factor profiling in expression genomics / Joseph E. Lucas [and others] -- Proportional hazards regression using Bayesian kernel machines / Arnab Maity and Bani K. Mallick -- A Bayesian mixture model for protein biomarker discovery / Peter Müller [and others] -- Bayesian methods for detecting differentially expressed genes / Fang Yu, Ming-Hui Chen, and Lynn Kuo -- Bayes and empirical Bayes methods for spotted microarray data analysis / Dabao Zhang -- Bayesian Classification method for QTL mapping / Min Zhang.

Bayesian Modeling in Bioinformatics 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. The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping. Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.