Thanks to the availability of high-throughput omics data, bioinformatics approaches are able to hypothesize thus-far undocumented genetic interactions. However, due to the amount of noise in these data, inferences based on a single data source are often unreliable. A popular approach to overcome this problem is to integrate different data sources. In this study, we describe DISTILLER, a novel framework for data integration that simultaneously analyzes microarray and motif information to find modules that consist of genes that are co-expressed in a subset of conditions, and their corresponding regulators. By applying our method on publicly available data, we evaluated the condition-specific transcriptional network of Escherichia coli. DISTILLER confirmed 62% of 736 interactions described in RegulonDB, and 278 novel interactions were predicted.
|Journal||Annals of the New York Academy of Sciences|
|State||Published - 11 Mar 2009|
|Event||DREAM reverse engineering challenges - Broad Institute of MIT and Harvard; the MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts|
Duration: 29 Oct 2008 → 2 Nov 2008