Bayesian Modeling in Bioinformatics (Google eBook)

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Dipak K. Dey, Samiran Ghosh, Bani K. Mallick
CRC Press, Sep 3, 2010 - Mathematics - 466 pages
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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.


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Estimation and Testing inTimeCourse Microarray Experiments
Classification for Differential GeneExpression Using Bayesian Hierarchical Models
Applications of the Mode OrientedStochastic Search MOSSAlgorithm for Discrete MultiWay Data to Genomewide Studies
Nonparametric Bayesian Bioinformatics
Measurement Error and Survival Model for cDNA Microarrays
Bayesian Robust Inference for Differential Gene Expression
Bayesian Hidden Markov Modeling of Array CGH Data
Bayesian Approaches to Phylogenetic Analysis
Learning Bayesian Networks for Gene Expression Data
InVitro to InVivo Factor Profiling in Expression Genomics
Proportional Hazards Regression Using Bayesian Kernel Machines
A Bayesian Mixture Model for Protein Biomarker Discovery
Bayesian Methods for Detecting Differentially Expressed Genes
Bayes and Empirical Bayes Methodsfor Spotted Microarray Data Analysis
Bayesian Classification Method for QTL Mapping

Gene Selection for the Identificationof Biomarkers in HighThroughput Data
Sparsity Priors for ProteinProtein Interaction Predictions
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About the author (2010)

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.

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