Bayesian Modeling in Bioinformatics Contributor(s): Dey, Dipak K. (Editor), Ghosh, Samiran (Editor), Mallick, Bani K. (Editor) |
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ISBN: 1420070177 ISBN-13: 9781420070170 Publisher: CRC Press OUR PRICE: $171.00 Product Type: Hardcover - Other Formats Published: September 2010 Annotation: One of the most comprehensive published references on this topic to date, this volume fills the gap in the vast but scattered literature on Bayesian bioinformatics and biostatistics. It presents the perspectives of Bayesian experts on important developments in bioinformatics and biostatistics from two unique standpoints. The book first looks at how to conceptualize, perform, and criticize traditional bioinformatics and biostatistics from a Bayesian perspective. Secondly, it examines how to use modern computational methods to summarize inferences using simulation. The book also explores emerging research topics. |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - Bayesian Analysis - Science | Biotechnology |
Dewey: 570.285 |
LCCN: 2009049470 |
Series: Chapman & Hall/CRC Biostatistics |
Physical Information: 1.1" H x 6.2" W x 9.3" (1.76 lbs) 466 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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. |