RASMA: A Reverse Search Algorithm for Mining Frequent Subgraphs

Abstract

Background: Mining frequent co-expression networks enables the discovery of interesting network motifs that elucidate important interactions among genes. Such interaction subnetworks have been shown to enhance the discovery of biological modules and subnetwork signatures for gene expression and disease classification. Results: We propose a reverse search algorithm for mining frequent and maximal subgraphs over a collection of graphs. We develop an approach for enumerating connected edge-induced subgraphs of an undirected graph by using a reverse-search algorithm, and then use this enumeration strategy for mining all maximal frequent subgraphs. To overcome the computationally prohibitive task of enumerating all frequent subgraphs while mining for maximal subgraphs, the proposed algorithm employs several pruning strategies, which substantially improve its overall runtime performance. Experimental results show that on large gene coexpression networks, the proposed algorithm efficiently mines biologically relevant maximal frequent subgraphs. Conclusion: Extracting recurrent gene coexpression subnetworks from multiple gene expression experiments enables the discovery of functional modules and subnetwork biomarkers. We have proposed a reverse search algorithm for mining maximal frequent subnetworks. Enrichment analysis of the extracted maximal frequent subnetworks reveals that subnetworks that are more frequent are more likely to be enriched with biological ontologies.