Marina Vannucci - Rice University

Title: Bayesian Models for Integrative Genomics. Abstract: Novel methodological questions are now being generated in Bioinformatics and require the integration of different concepts, methods, tools and data types. Bayesian methods that employ variable selection have been particularly successful for genomic applications, as they allow to handle situations where the amount of measured variables can be much greater than the number of observations. In this talk I will first describe Bayesian variable selection models that incorporate external biological information into the analysis of gene expression data. I will consider linear settings, including regression and classification models, and mixture models, including clustering and discriminant analysis. If time allows it, I will also introduce Bayesian models that achieve an even greater type of integration, by incorporating into the modeling experimental data from different platforms, together with prior knowledge. I will look in particular at graphical models, integrating gene expression data with microRNA expression data. All modeling settings employ variable selection techniques and prior constructions that cleverly incorporate biological knowledge about structural dependencies among the variables.