UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
Improved Bayesian Network inference using relaxed gene ordering
International Journal of Data Mining and Bioinformatics
Inference of gene regulatory network using modified genetic algorithm
ISB '10 Proceedings of the International Symposium on Biocomputing
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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).