Items related to Bayesian Inference for Gene Expression and Proteomics
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.
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Book Description
A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation 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 Author
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.
"About this title" may belong to another edition of this title.
- PublisherCambridge University Press
- Publication date2006
- ISBN 10 052186092X
- ISBN 13 9780521860925
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages456
- EditorDo Kim-Anh, Müller Peter, Vannucci Marina
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Bayesian Inference for Gene Expression and Proteomics
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