Bayesian Inference for Gene Expression and Proteomics
Published on 30. April 2012
Book
Paperback/Softback
456 pages
978-1-107-63698-9 (ISBN)
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 / Votes
'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, BiometricsMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Paperback (trade)
Illustrations
22 Tables, unspecified
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 25 mm
Weight
672 gr
ISBN-13
978-1-107-63698-9 (9781107636989)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kim-Anh Do | Peter Mueller | Marina Vannucci
Bayesian Inference for Gene Expression and Proteomics
Edited by Kim-Anh Do and Peter Müller
Book
07/2006
Cambridge University Press
€73.50
No shipping information available
Persons
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 Mueller 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.
Content
1. An introduction to high-throughput bioinformatics data Keith Baggerly, Kevin Coombes and Jeffrey S. Morris; 2. Hierarchical mixture models for expression profiles Michael Newton, Ping Wang and Christina Kendziorski; 3. Bayesian hierarchical models for inference in microarray data Anne-Mette K. Hein, Alex Lewin and Sylvia Richardson; 4. Bayesian process-based modeling of two-channel microarray experiments estimating absolute mRNA concentrations Mark A. van de Wiel, Marit Holden, Ingrid K. Glad, Heidi Lyng and Arnoldo Frigessi; 5. Identification of biomarkers in classification and clustering of high-throughput data Mahlet Tadesse, Marina Vannucci, Naijun Sha and Sinae Kim; 6. Modeling nonlinear gene interactions using Bayesian MARS Veerabhadran Baladandayuthapani, Chris C. Holmes, Bani K. Mallick and Raymond J. Carroll; 7. Models for probability of under- and over-expression: the POE scale Elizabeth Garrett-Mayer and Robert Scharpf; 8. Sparse statistical modelling in gene expression genomics Joseph Lucas, Carlos Carvalho, Quanli Wang, Andrea Bild, Joseph Nevins and Mike West; 9. Bayesian analysis of cell-cycle gene expression Chuan Zhou, Jon Wakefield and Linda L. Breeden; 10. Model-based clustering for expression data via a Dirichlet process mixture model David Dahl; 11. Interval mapping for Expression Quantitative Trait Loci mapping Meng Chen and Christina Kendziorski; 12. Bayesian mixture model for gene expression and protein profiles Michele Guindani, Kim-Anh Do, Peter Mueller and Jeffrey S. Morris; 13. Shrinkage estimation for SAGE data using a mixture Dirichlet prior Jeffrey S. Morris, Kevin Coombes and Keith Baggerly; 14. Analysis of mass spectrometry data using Bayesian wavelet-based functional mixed models Jeffrey S. Morris, Philip J. Brown, Keith Baggerly and Kevin Coombes; 15. Nonparametric models for proteomic peak identification and quantification Merlise Clyde, Leanna House and Robert Wolpert; 16. Bayesian modeling and inference for sequence motif discovery Mayetri Gupta and Jun S. Liu; 17. Identifying of DNA regulatory motifs and regulators by integrating gene expression and sequence data Deuk Woo Kwon, Sinae Kim, David Dahl, Michael Swartz, Mahlet Tadesse and Marina Vannucci; 18. A misclassification model for inferring transcriptional regulatory networks Ning Sun and Hongyu Zhao; 19. Estimating cellular signaling from transcription data Andrew V. Kossenkov, Ghislain Bidaut and Michael Ochs; 20. Computational methods for learning Bayesian networks from high-throughput biological data Bradley Broom and Devika Subramanian; 21. Modeling transcriptional regulation: Bayesian networks and informative priors Alexander J. Hartemink; 22. Sample size choice for microarray experiments Peter Mueller, Christian Robert and Judith Rousseau.