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  1. Home
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  3. Bayesian Inference for Gene Expression and Proteomics
  • Cited by 20
Publisher:
Cambridge University Press
Online publication date:
November 2009
Print publication year:
2006
Online ISBN:
9780511584589
DOI:
https://doi.org/10.1017/CBO9780511584589
Subjects:
Genomics, Bioinformatics and Systems Biology, Statistics for Life Sciences, Medicine and Health, Life Sciences, Statistics and Probability
Information

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.

Reviews

‘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.'

Source: 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 Source: 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 Source: Biometrics

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