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Bayesian modeling in bioinformatics

Dey, Dipak.

Bayesian modeling in bioinformatics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian modeling in bioinformaticsedited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick.
other author:	Dey, Dipak.
Published:	Boca Raton :CRC Press,c2011.
Description:	1 online resource (xxv, 440 p.) :ill.
Notes:	"A Chapman & Hall book."
Subject:	Bayes Theorem.
Online resource:	http://www.crcnetbase.com/isbn/978-1-4200-7017-0
ISBN:	9781420070187 (electronic bk.)

Bayesian modeling in bioinformatics
Bayesian modeling in bioinformatics[electronic resource] /edited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick. - Boca Raton :CRC Press,c2011. - 1 online resource (xxv, 440 p.) :ill. - Chapman & Hall/CRC biostatistics series ;34. - Chapman & Hall/CRC biostatistics series ;38..

"A Chapman & Hall book."

Includes bibliographical references and index.

List of Tables -- List of Figures -- Preface -- Symbol Description -- Chapter 1: Estimation and Testing in Time-Course Microarray Experiments -- Chapter 2: Classification for Differential Gene Expression Using Bayesian Hierarchical Models -- Chapter 3: Applications of the Mode Oriented Stochastic Search (MOSS) Algorithm for Discrete Multi-Way Data to Genomewide Studies -- Chapter 4: Nonparametric Bayesian Bioinformatics -- Chapter 5: Measurement Error and Survival Model for cDNA Microarrays -- Chapter 6: Bayesian Robust Inference for Differential Gene Expression.

"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.

ISBN: 9781420070187 (electronic bk.)Subjects--Topical Terms:

230798
Bayes Theorem.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QH324.2 / .D49 2011eb

Dewey Class. No.: 570.285

National Library of Medicine Call No.: QH 324.2

Bayesian modeling in bioinformatics
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050 4 $a QH324.2 $b .D49 2011eb
060 4 $a QH 324.2
082 0 4 $a 570.285 $2 22
245 0 0 $a Bayesian modeling in bioinformatics $h [electronic resource] / $c edited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick.
260 $a Boca Raton : $b CRC Press, $c c2011.
300 $a 1 online resource (xxv, 440 p.) : $b ill.
490 1 $a Chapman & Hall/CRC biostatistics series ; $v 34
500 $a "A Chapman & Hall book."
504 $a Includes bibliographical references and index.
505 0 $a List of Tables -- List of Figures -- Preface -- Symbol Description -- Chapter 1: Estimation and Testing in Time-Course Microarray Experiments -- Chapter 2: Classification for Differential Gene Expression Using Bayesian Hierarchical Models -- Chapter 3: Applications of the Mode Oriented Stochastic Search (MOSS) Algorithm for Discrete Multi-Way Data to Genomewide Studies -- Chapter 4: Nonparametric Bayesian Bioinformatics -- Chapter 5: Measurement Error and Survival Model for cDNA Microarrays -- Chapter 6: Bayesian Robust Inference for Differential Gene Expression.
520 $a "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.
588 $a Description based on print version record.
650 1 2 $a Bayes Theorem. $3 230798
650 2 2 $a Computational Biology. $3 220388
650 2 2 $a Models, Biological. $3 237152
650 0 $a Bioinformatics $x Statistical methods. $3 239219
650 0 $a Bayesian statistical decision theory. $3 182005
655 0 $a Electronic books. $2 local. $3 214472
700 1 $a Dey, Dipak. $3 219105
700 1 $a Ghosh, Samiran. $3 508420
700 1 $a Mallick, Bani K., $d 1965- $3 508421
830 0 $a Chapman & Hall/CRC biostatistics series ; $v 38. $3 487298
856 4 0 $u http://www.crcnetbase.com/isbn/978-1-4200-7017-0