MFMS: maximal frequent module set mining from multiple human gene expression data sets

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.