Bayesian Inference for Gene Expression and Proteomics Paperback / softback
Edited by Kim-Anh (University of Texas, MD Anderson Cancer Center) Do, Peter (Swiss Federal Institute of Technology, Zurich) Muller, Marina (Rice University, Houston) Vannucci
Paperback / softback
Description
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.
Information
-
Out of stock
- Format:Paperback / softback
- Pages:456 pages, 22 Tables, unspecified
- Publisher:Cambridge University Press
- Publication Date:30/04/2012
- Category:
- ISBN:9781107636989
Information
-
Out of stock
- Format:Paperback / softback
- Pages:456 pages, 22 Tables, unspecified
- Publisher:Cambridge University Press
- Publication Date:30/04/2012
- Category:
- ISBN:9781107636989
