Journal
CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY
Volume 1158, Issue -, Pages 29-35Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1749-6632.2008.03746.x
Keywords
transcriptional modules; frequent itemset mining; DISTILLER
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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 frame work 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 v,,ere predicted.
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