Speaker: Amira Djebbari Construction of genetic networks from the biological literature and microarray data Increasingly, we are coming to realize that genes interact with each other in complex networks and that these networks are what underlie the phenotypes we observe. DNA microarrays and other technologies allow the measurement of the expression patterns of thousands of genes in a single assay. The challenge is no longer collecting data, but rather interpreting it and extracting biologically relevant networks. Although many techniques have been developed to deal with microarray data, to date their ability to extract network relationships has been limited. Our goal is to extract biologically relevant pathways from microarray data and develop testable hypotheses that could then be validated in the laboratory. Our approach consists of using networks derived from the literature and/or protein-protein interaction data as constraints on a Bayesian network analysis of microarray results. We show that high confidence interactions can be learned by a bootstrap method when applying this approach to leukemia datasets. These high-confidence interactions found by combining literature and microarray data can most often recover well-known pathways, validating this approach, even in experiments not explicitly designed to probe them. Software implementing these methods is under development for inclusion in the widely used TM4 microarray analysis package.