Published online 17 May 2004
Nucleic Acids Research, 2004, Vol. 32, No. 9 2685-2694
Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling
1 Department of Biomedical Engineering, Biomathematics and Bioinformatics, Harbin Medical University, Harbin 150086, China, 2 Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China and 3 Departments of Molecular Cardiology and Cardiovascular Medicine, the Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA
*To whom correspondence should be addressed. Tel: +1 216 444 0056; Fax: +1 216 444 2682; Email: raos{at}ccf.orgCorrespondence may also be addressed to Xia Li. Tel: +86 451 8661 5922; Fax: +86 451 8666 9617; Email: lixia{at}ems.hrbmu.edu.cn
Received December 12, 2003; Revised February 29, 2004; Accepted April 2, 2004
Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks. An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia phenotype, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore, the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.
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