Bayesian Modeling in Bioinformatics

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.

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.

Estimation and Testing in Time-Course Microarray Experiments, C. Angelini, D. De Canditilis, and M. Pensky

Classification for Differential Gene Expression Using Bayesian Hierarchical Models, N. Bochkina and A. Lewin

Applications of the Mode Oriented Stochastic Search (MOSS) for Discrete Multi-Way Data to Genome-Wide Studies, A. Dobra, L. Briollais, H. Jarjanazi, H. Ozelik, and H. Massam

Nonparametric Bayesian Bioinformatics, D. Dunson

Measurement Error Models for cDNA Microarray and Time-to-Event Data with Applications to Breast Cancer, J. Gelfond and J. Ibrahim

Bayesian Robust Inference for Differential Gene Expression, R. Gottardo

Bayesian Hidden Markov Modeling of Array CGH Data, S. Guha

Recent Developments in Bayesian Phylogenetics, M. Holder, J. Sukumaran, and R. Brown

Gene Selection for the Identification of Biomarkers in High-Throughput Data, J. Jeong, M. Vannucci, K. Do, B. Broom, S. Kim, N. Sha, M. Tadese, K. Yan, and L. Puzstai

Sparsity Priors for Protein-Protein Interaction Predictions, I. Kim, Y. Liu, and H. Zhao

Learning Bayesian Networks for Gene Expression Data, F. Liang
In Vitro to In Vivo Factor Profiling in Expression Genomics, J. Lucas, C, Carvalho, D. Merl, and M. West

Proportional Hazards Regression Using Bayesian Kernel Machines, A. Maity and B. Mallick

A Bayesian Mixture Model for Protein Biomarker Discovery, P. Muller, K. Baggerly, K. Do, and Bandopadhyay

Bayesian Methods for Detecting Differentially Expressed Genes, F. Yu, M-H. Chen, and L. Kuo

Bayes and Empirical Bayes Methods for Spotted Microarray Data Analysis, D. Zhang

Bayesian Classification Method for QTL Mapping, M. Zhang