Fordham InformatICS Seminar
A tripartite clustering analysis on microRNA, gene and disease model
Speaker: Dr. Ying Liu, Division of Computer Science, St. John's University
Date: April 7, 2014
Time: 1:00-2:00pm
Location: JMH 112
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.
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