- 10:00 am on Friday, November 2, 2012
- 11:00 am on Friday, November 2, 2012
- MCS B25 (basement)
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.