Bayesian Inference for Gene Expression and Proteomics

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Marina Vannucci Edited by Kim-Anh DoCambridge University Press, 4/30/2012EAN 9781107636989, ISBN10: 1107636981Paperback, 456 pages, 22.1 x 14.1 x 2.5 cmLanguage: EnglishThe interdi sciplinary 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.1. An introduction to high-throughput bioinformatics data Keith Baggerly, Kevin Coombes and Jeffrey S. Morris2. Hierarchical mixture models for expression profiles Michael Newton, Ping Wang and Christina Kendziorski3. Bayesian hierarchical models for inference in microarray data Anne-Mette K. Hein, Alex Lewin and Sylvia Richardson4. 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 Frigessi5. Identification of biomarkers in classification and clustering of high-throughput data Mahlet Tadesse, Marina Vannucci, Naijun Sha and Sinae Kim6. Modeling nonlinear gene interactions using Bayesian MARS Veerabhadran Baladandayuthapani, Chris C. Holmes, Bani K. Mallick and Raymond J. Carroll7. Models for probability of under- and over-expressionthe POE scale Elizabeth Garrett-Mayer and Robert Scharpf8. Sparse statistical modelling in gene expression genomics Joseph Lucas, Carlos Carvalho, Quanli Wang, Andrea Bild, Joseph Nevins and Mike West9. Bayesian analysis of cell-cycle gene expression Chuan Zhou, Jon Wakefield and Linda L. Breeden10. Model-based clustering for expression data via a Dirichlet process mixture model David Dahl11. Interval mapping for Expression Quantitative Trait Loci mapping Meng Chen and Christina Kendziorski12. Bayesian mixture model for gene expression and protein profiles Michele Guindani, Kim-Anh Do, Peter Müller and Jeffrey S. Morris13. Shrinkage estimation for SAGE data using a mixture Dirichlet prior Jeffrey S. Morris, Kevin Coombes and Keith Baggerly14. Analysis of mass spectrometry data using Bayesian wavelet-based functional mixed models Jeffrey S. Morris, Philip J. Brown, Keith Baggerly and Kevin Coombes15. Nonparametric models for proteomic peak identification and quantification Merlise Clyde, Leanna House and Robert Wolpert16. Bayesian modeling and inference for sequence motif discovery Mayetri Gupta and Jun S. Liu17. 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 Vannucci18. A misclassification model for inferring transcriptional regulatory networks Ning Sun and Hongyu Zhao19. Estimating cellular signaling from transcription data Andrew V. Kossenkov, Ghislain Bidaut and Michael Ochs20. Computational methods for learning Bayesian networks from high-throughput biological data Bradley Broom and Devika Subramanian21. Modeling transcriptional regulationBayesian networks and informative priors Alexander J. Hartemink22. Sample size choice for microarray experiments Peter Müller, Christian Robert and Judith Rousseau.'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, Department of Statistics and Institute for Genomic Biology University of Illinois at Urbana-Champaign Show more