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 Müller 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 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, University of Illinois, Urbana-Champaign