Programming Computers to Identify Genes and Interpret their Functional Roles
Recent breakthroughs in high-throughput biology provide us with a wealth of information derived by novel experimental methods which include sequencing, microarrays, protein-protein interaction maps and others techniques to interrogate biological cells. In this talk we outline a probabilistic framework for representing and fusing this information in the goal inferring the biological function of genes from genomic data supported by a set of partial models and databases. The key computational ideas we use are machine learning, graph representations and probabilistic networks. The biological problems we will address in the talk include pathway inference, comparative genomics of closely related organisms, and inference of gene function using protein-protein interaction networks and DNA chips.