Learning gene regulatory networks via globally regularized risk minimization
Learning the structure of a gene regulatory network from time-series gene expression data is a significant challenge. Most approaches proposed in the literature to date attempt to predict the regulators of each target gene individually, but fail to share regulatory information between related genes. In this paper, we propose a new globally regularized risk minimization approach to address this problem. Our approach first clusters genes according to their time-series expression profiles-identifying related groups of genes. Given a clustering, we then develop a simple technique that exploits the assumption that genes with similar expression patterns are likely to be co-regulated by encouraging the genes in the same group to share common regulators. Our experiments on both synthetic and real gene expression data suggest that our new approach is more effective at identifying important transcription factor based regulatory mechanisms than the standard independent approach and a prototype based approach.