Integration of Co-expression Networks for Gene Clustering

Simultaneous overexpression or underexpression of multiple genes, used in various forms as probes in the high-throughput microarray experiments, facilitates the identification of their underlying functional proximity. This kind of functional associativity (or conversely the separability) between the genes can be represented proficiently using co-expression networks. The extensive repository of diversified microarray data encounters a recent problem of multi-experimental data integration for the aforesaid purpose. This paper highlights a novel integration method of gene co-expression networks, based on the search for their consensus network, derived from diverse microarray experimental data for the purpose of clustering. The proposed methodology avoids the bias arising from missing value estimation. The method has been applied on microarray datasets arising from different category of experiments to integrate them. The consensus network, thus produced, reflects robustness based on biological validation

Mining Co-expression Graphs: Applications to MicroRNA Regulation and Disease Analysis

MicroRNAs (miRNAs) are known to translationally repress or post-transcriptionally regulate mRNAs and are responsible for many diseases. We are preparing a comprehensive framework of co-expression analysis to figure out co-expression, differential co-expression and co-expression dynamics within multiple phenotypes in expression profiles. The purpose is to elucidate the disease association of miRNAs via co-regulatory pattern analysis