With the availability of vast amounts of protein-protein, protein-DNA interactions, and genome-wide mRNA expression data for several organisms, identifying biological complexes has emerged as a major task in systems biology. Most of the existing approaches for complex identification have focused on utilizing one source of data. Recent research has shown that systematic integration of gene profile data with interaction data yields significant patterns. In this paper, we introduce the problem of mining maximal cohesive subnetworks that satisfy user-defined constraints defined over the gene profiles of the reported subnetworks. Moreover, we introduce the problem of finding maximal cohesive patterns which are sets of cohesive genes. Experiments on Yeast and Human datasets show the effectiveness of the proposed approach by assessing the overlap of the discovered subnetworks with known biological complexes. Moreover, GO enrichment analysis shows that the discovered subnetworks are biologically significant.
|Evidence ID||Analyze ID||Interactor||Interactor Systematic Name||Interactor||Interactor Systematic Name||Type||Assay||Annotation||Action||Modification||Phenotype||Source||Reference||Note|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Gene Ontology Term||Gene Ontology Term ID||Qualifier||Aspect||Method||Evidence||Source||Assigned On||Annotation Extension||Reference|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Phenotype||Experiment Type||Experiment Type Category||Mutant Information||Strain Background||Chemical||Details||Reference|
|Evidence ID||Analyze ID||Regulator||Regulator Systematic Name||Target||Target Systematic Name||Experiment||Assay||Construct||Conditions||Strain Background||Reference|