Bayesian modeling in bioinformatics [electronic resource]

• 其他作者：
  • Dey, Dipak.
  • Ghosh, Samiran.
  • Mallick, Bani K., 1965-
• 其他題名：
  • Chapman & Hall/CRC biostatistics series
• 出版： Boca Raton : CRC Press
• 叢書名： Chapman & Hall/CRC biostatistics series34
• 主題： Bayes Theorem. , Computational Biology , Models, Biological , Bioinformatics--Statistical methods. , Bayesian statistical decision theory , Electronic books.
• ISBN： 9781420070187 (electronic bk.) 、 1420070185 (electronic bk.)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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. "A Chapman & Hall book."
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• 摘要註: "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.
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