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

Authors:
Saeed Salem;Cagri Ozcaglar
Affiliations:
North Dakota State University, Fargo, ND;Bank of America Merrill Lynch
Venue:
Proceedings of the 12th International Workshop on Data Mining in Bioinformatics
Year:
2013

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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.