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  1. ; (, BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)
    null (Ed.)
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  2. ; (, BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)
    null (Ed.)
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  3. ; (, EPiC Series in Computing)
    Large amount of gene expression data has been collected for various environmental and biological conditions. Extracting co-expression networks that are recurrent in multiple co-expression networks has been shown promising in functional gene annotation and biomarkers discovery. Frequent subgraph mining reports a large number of subnetworks. In this work, we propose to mine approximate dense frequent subgraphs. Our proposed approach reports representative frequent subgraphs that are also dense. Our experiments on real gene coexpression networks show that frequent subgraphs are biologically interesting as evidenced by the large percentage of biologically enriched frequent dense subgraphs. 
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