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Bayesian modeling in bioinformatics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian modeling in bioinformatics/ edited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick.
其他作者:	Dey, Dipak.
出版者:	Boca Raton :CRC Press, : c2011.,
面頁冊數:	1 online resource (xxv, 440 p.) :ill. :
附註:	"A Chapman & Hall book."
標題:	Bayes Theorem. -
電子資源:	https://www.taylorfrancis.com/books/9780429141461
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:

970513
Bayes Theorem.
Index Terms--Genre/Form:

554714
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
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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 970513
650 2 2 $a Computational Biology. $3 581380
650 2 2 $a Models, Biological. $3 591633
650 0 $a Bioinformatics $x Statistical methods. $3 672232
650 0 $a Bayesian statistical decision theory. $3 527671
655 0 $a Electronic books. $2 local $3 554714
700 1 $a Dey, Dipak. $3 970518
700 1 $a Ghosh, Samiran. $3 970519
700 1 $a Mallick, Bani K., $d 1965- $3 970520
830 0 $a Chapman & Hall/CRC biostatistics series ; $v 38. $3 848174
856 4 0 $u https://www.taylorfrancis.com/books/9780429141461