Meta analysis algorithms for microarray gene expression data using gene regulatory networks.
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.
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-2237, USA. saira@engr.uconn.edu
Saira A
SA
Kazmi
Yoo-Ah
YA
Kim
Dong-Guk
DG
Shin
D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D015201:Meta-Analysis as Topic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*