Bayesian Inference for Gene Expression and

The interdisciplinary nature of bioinformatics presents a challenge in integrating concepts, methods, software, and multi-platform data. Although there have been rapid developments in new technology and an inundation of statistical methodology and software for the analysis of microarray gene expression arrays, there exist few rigorous 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 from medical research and molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical models. 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.

An introduction to high-throughput bioinformatics data
Hierarchical mixture models for expression profiles
Bayesian hierarchical models for inference in microarray data
Bayesian process-based modeling of two-channel microarray experiments estimating absolute mRNA concentrations
Identification of biomarkers in classification and clustering of high-throughput data
Modeling nonlinear gene interactions using Bayesian MARS
Models for probability of under- and over-expression: the POE scale
Sparse statistical modelling in gene expression genomics
Bayesian analysis of cell-cycle gene expression
Model-based clustering for expression data via a Dirichlet process mixture model
Interval mapping for Expression Quantitative Trait Loci mapping
Bayesian mixture model for gene expression and protein profiles
Shrinkage estimation for SAGE data using a mixture Dirichlet prior
Analysis of mass spectrometry data using Bayesian wavelet-based functional mixed models
Nonparametric models for proteomic peak identification and quantification Merlise Clyde, House
Bayesian modeling and inference for sequence motif discovery
Identifying of DNA regulatory motifs and regulators by integrating gene expression and sequence data
A misclassification model for inferring transcriptional regulatory networks
Estimating cellular signaling from transcription data
Computational methods for learning Bayesian networks from high-throughput biological data
Modeling transcriptional regulation: Bayesian networks and informative priors
Sample size choice for microarray experiments
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Bayesian Inference for Gene Expression and Proteomics

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