MFMS

Abstract

Advances in genomic technologies have allowed vast amounts of gene expression data to be collected. Protein functional annotation and biological module discovery that are based on a single gene expression data suffers from spurious coexpression. Recent work have focused on integrating multiple independent gene expression data sets. In this paper, we propose a two-step approach for mining maximally frequent collection of highly connected modules from coexpression graphs. We first mine maximal frequent edge-sets and then extract highly connected subgraphs from the edge-induced subgraphs. Experimental results on the collection of modules mined from 52 Human gene expression data sets show that coexpression links that occur together in a significant number of experiments have a modular topological structure. Moreover, GO enrichment analysis shows that the proposed approach discovers biologically significant frequent collections of modules.