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Book 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.

Book details

Book category Books in English Mathematics & science Mathematics Probability & statistics

• Full title: Bayesian Inference for Gene Expression and Proteomics
• Author: Kim-Anh DoPeter MüllerMarina Vannucci
• Language: English
• Binding: Hardback
• Number of pages: 456
• EAN: 9780521860925
• ISBN: 052186092X
• ID: 02047665
• Publisher: Cambridge University Press
• Weight: 744 g
• Dimensions: 236 × 160 × 29 mm
• Date of publishing: 24. July 2006

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