Bayesian Inference for Gene Expression and Proteomics
Author(s): Kim-Anh Do
Publisher: Cambridge University Press
Publication Date: 5 July 2012
Language: English
Print length: 468 pages
ISBN-10: 1107636981
ISBN-13: 9781107636989
Book 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.
Editorial Reviews
Review
'A text that has a systematic account of Bayesian analysis in computational biology has been needed for a long time. This book is a timely publication entirely devoted to cutting-edge Bayesian methods in genomics and proteomics research and many of its contributors are leading authorities in the field. It is thus an indispensable reference for researchers who are interested in applying Bayesian techniques in their own biological research.' Ping Ma, University of Illinois, Urbana-Champaign
'… an authoritative volume … presents the state of the art statistical techniques that are starting to make an impact at the forefronts of modern scientific discovery.' Journal of the RSS
'A collection of 22 high quality chapters, authored by several distinguished groups of academic researchers … researchers and students will appreciate an authoritative volume like the present.' Z. Q. John Lu, National Institute of Standards and Technology, Gaithersburg
'The editors have done a great job keeping the writing of diverse authors readable without great redundancy … This book should be required reading for all graduate students of statistics, statistical researchers in this field, and students and researchers in other fields that use these technologies.' Tapabrata Maita, Journal of the American Statistician
'Overall, I find this text an excellent contribution to the literature on statistical methods for high throughput genomic and proteomic data analysis. The chapters are well written, the case studies are informative, and the range of topics covered is quite broad and generally logically grouped. I would highly recommend this text to both those people already working in the area and those wanting to break in. It is not only suitable for researchers developing their own methodologies but also for applied quantitative scientists looking for the most cutting-edge tools to analyze their high throughput datasets.' J. Sunil Rao, Biometrics
Book Description
Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
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.
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.
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