Genes,Tumors, and Bayes nets: Improving the specificity of biological signal detection in microarray analysis.

Microarray analysis allows for large-scale exploration of gene expression by taking a snap shot of the cell at a specific point in time. Such data sets may provide insight into fundamental biological questions as well as address clinical issues such as diagnosis and therapy selection. The resulting data are complex and challenging to analyze without sophisticated computational tools. This seminar will highlight the issue of improving the specificity of biological signal detection from microarray data. I will present robust and accurate algorithms for missing value estimation for microarray data and for identification of differentially expressed genes from gene expression data sets. This talk will also address gene function prediction and present a Bayesian framework for integrated analysis of gene expression data with data sets from other high- throughput biological data sources.