Bayesian Inference for Gene Expression and Proteomics

. Ed(s): Do, Kim-Anh; Muller, Peter; Vannucci, Marina

ISBN 10: 1107636981 / ISBN 13: 9781107636989

Published by Cambridge University Press, 2012

About this Item

Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. Editor(s): Do, Kim-Anh; Muller, Peter; Vannucci, Marina. Num Pages: 456 pages, 22 tables. BIC Classification: PBT; PSAK. Category: (P) Professional & Vocational. Dimension: 220 x 140 x 25. Weight in Grams: 572. . 2012. Paperback. . . . .

Seller Inventory # V9781107636989

Bibliographic Details

• Title: Bayesian Inference for Gene Expression and Proteomics
• Publisher: Cambridge University Press
• Publication Date: 2012
• Language: English
• ISBN 10: 1107636981
• ISBN 13: 9781107636989
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.

About the Authors

Kim-Anh Do is a Professor in the Department of Biostatistics and Applied Mathematics at the University of Texas M. D. Anderson Cancer Center. Her research interests are in computer-intensive statistical methods with recent focus in the development of methodology and software to analyze data produced from high-throughput optimization.

Peter Müller is a Professor in the Department of Biostatistics and Applied Mathematics at the University of Texas M. D. Anderson Cancer Center. His research interests and contributions are in the areas of Markov chain Monte Carlo posterior simulation, nonparametric Bayesian inference, hierarchical models, mixture models and Bayesian decisions problems.

Marina Vannucci is a Professor of Statistics at Rice University. Her research focuses on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their applications. Her work is often motivated by real problems that need to be addressed with suitable statistical methods.