Mining approximate frequent dense modules from multiple gene expression datasets

10 pages•Published: March 11, 2020

Abstract

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.

Keyphrases: biclustering, frequent dense modules, gene co expression networks

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 129-138.

Links:	https://easychair.org/publications/paper/xkbX
	https://doi.org/10.29007/d87q