San Ha Seo
ABSTRACT
Gene expression data for multiple biological and environmental conditions is being collected for multiple species. Functional modules and subnetwork biomarkers discovery have traditionally been based on analyzing a single gene expression dataset. Research has focused on discovering modules from multiple gene expression datasets. Gene coexpression network mining methods have been proposed for mining frequent functional modules. Moreover, biclustering algorithms have been proposed to allow for missing coexpression links. Existing approaches report a large number of edgesets that have high overlap. In this work, we propose an algorithm to mine frequent dense modules from multiple coexpression networks using a post-processing data summarization method. Our algorithm mines a succinct set of representative subgraphs that have little overlap which reduce the downstream analysis of the reported modules. Experiments on human gene expression data show that the reported modules are biologically significant as evident by Gene Ontology molecular functions and KEGG pathways enrichment.
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- Post-Processing Summarization for Mining Frequent Dense Subnetworks
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