Disease gene explorer: display disease gene dependency by combining Bayesian networks with clustering

Constructing gene networks is one of the hot topics in the analysis of the microarray gene expression data. When combined with the output of disease gene finding, the generated gene networks will give a recommendation mechanism and an intuitive form for biologists to identify the underlying relationship among those biomarkers of the disease. In this paper, we present a display system, disease gene explorer, which can graphically display the dependency among genes, especially those biomarkers of a disease. It combines Bayesian networks (BN) learning with clustering and disease gene selection. We test the system on colon cancer data set and obtain some interesting results: most high-score biomarkers of the disease are partitioned into one group; the dependency among these disease genes are displayed as a directed acyclic graph (DAG).