A tripartite clustering analysis on microRNA, gene and disease model

Authors:
Chengcheng Shen;Ying Liu
Affiliations:
University of Texas at Dallas, Richardson, TX;University of North Texas at Dallas, Dallas, TX
Venue:
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2011

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Abstract

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and can lead to a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than random clusters. Results also show that miRNAs in the same family tend to belong to the same cluster. We further validate the results by checking the correlation of enriched gene functions and diseases in the same cluster. Finally miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.