An ensemble method for gene discovery based on DNA microarray data |
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Authors: | LI Xia RAO Shaoqi ZHANG Tianwen GUO Zheng ZHANG Qingpu Kathy L. MOSER Eric J. TOPOL |
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Affiliation: | Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. Lixia@ems.hrbmu.edu.cn |
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Abstract: | The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simulta-neously. Current analyses of microarray data focus on precise classification of biological types, for example, tumor versus normal tissues. A further scientific challenging task is to extract dis-ease-relevant genes from the bewildering amounts of raw data, which is one of the most critical themes in the post-genomic era, but it is generally ignored due to lack of an efficient approach. In this paper, we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-driven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii). |
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Keywords: | microarrays ensemble decision recursive partition tree feature gene selection |
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