Mining representative approximate frequent coexpression subnetworks

Advances in high-throughput microarray and RNA-sequencing technologies have lead to a rapid accumulation of gene expression data for various biological conditions across multiple species. Mining frequent gene modules from a set of multiple gene coexpression networks has applications in functional gene annotation and biomarker discovery. Biclustering algorithms have been proposed to allow for missing coexpression links. Existing approaches report a large number of edgesets which are computationally intensive to analyze, and have high overlap among the reported subnetworks. In this work, we propose an algorithm to mine frequent dense modules from multiple coexpression networks using an online data summarization method. Our algorithm mines a succinct set of representative subgraphs that have little overlap which reduces the downstream analysis of the reported modules. Experiments on human gene expression data show that the reported modules are biologically significant as evident by the high enrichment of GO molecular functions and KEGG pathways in the reported modules.