Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks

Authors:
Saira A. Kazmi;Yoo-Ah Kim;Dong-Guk Shin
Affiliations:
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-2237, USA.;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-2237, USA.;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-2237, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

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Abstract

Using microarrays, researchers are able to obtain a genome wide snapshot of a biological system under a given experimental context. Fortunately, a significant amount of gene regulation data is publicly available through various databases. We present a system that uses extra knowledge in published gene regulation relationships to examine findings in a microarray experiment and to aid biologists in generating hypotheses. Two algorithms are developed to highlight consistencies as well as inconsistencies between the data. We demonstrate that consistent as well as inconsistent subnetworks found in this manner are important in the discovery of active pathways and novel findings.