Bayesian Inference for Gene Expression and Proteomics

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.

Title from publishers bibliographic system (viewed on 10 Sep 2010).

Machine derived contents note: 1. An Introduction to High-Throughput Bioinformatics Data, Keith Baggerly, Kevin Coombes, Jeffrey S. Morris; 2. Hierarchical Mixture Models for Expression Profiles, Michael Newton, Ping Wang, Christina Kendziorski; 3. Bayesian Hierarchical Models for Inference in Microarray Data, Hein, Lewin, Silvia Richardson; 4. Bayesian Process-based Modeling of Two-Channel Microarray Experiments Estimating Absolute mRNA Concentrations, van de Wiel, Holden, Glad, Lyng, Frigessi; 5. Identification of biomarkers in classification and clustering of high-throughput data, Tadesse, Marina Vannucci, Sha, Kim; 6. Modeling nonlinear gene interactions using Bayesian MARS, Baladandayuthapani, Chris C. Holmes, Bani K. Mallick, Raymond J. Carroll; 7. Models for probability of under- and overexpression: The POE scale, Elizabeth Garrett-Mayer, Robert Scharpf; 8. Sparse Statistical Modeling in Gene Expression Genomics, Lucas, Carvalho, Wang, Bild, Nevins, West; 9. Bayesian analysis of cell-cycle gene expression, Zhou, Wakefield, Breeden; 10. Model-based clustering for expression data via Dirichlet process mixture model, Dahl; 11. Interval mapping for Expression Quantitative Trait…