A predictive model for identifying mini-regulatory modules in the mouse genome

Mahesh Yaragatti, Ted Sandler, Lyle H. Ungar

Abstract: Rapidly advancing genome technology has allowed access to a large number of diverse genomes and annotation data. We have defined a systems model that integrates assembly data, comparative genomics, gene predictions, mRNA and EST alignments and physiological tissue expression. Using these as predictive parameters, we engineered a machine learning approach to decipher putative active regions in the genome.