Mining maximal cohesive induced subnetworks and patterns by integrating biological networks with gene profile data

Abstract

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.

CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements

“…11 with the actual numbers of generated MCSs and MCS collections are illustrated in Table 5 . The first level of CoSREM, GenMCS, running time mainly depends on the number of discovered patterns as well as the number of explored branches [ 34 ]. For building the MCSTree , the time complexity is O (2 r ) in the worst case, where r is the number of MCSs.…”

“…Given an SRE graph G U and an SRE profile matrix P , the algorithm GenMCS from [ 34 ] is modified to find maximal α -cohesive subgraphs.…”