期刊
APPLIED INTELLIGENCE
卷 26, 期 3, 页码 243-250出版社
SPRINGER
DOI: 10.1007/s10489-006-0020-4
关键词
DNA microarray; gene expression data; cancer classification; ensemble classifier; multiple significant gene subsets
资金
- Korea Institute of Industrial Technology(KITECH) [10023913] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Molecular level diagnostics based on microarray technologies can offer the methodology of precise, objective, and systematic cancer classification. Genome-wide expression patterns generally consist of thousands of genes. It is desirable to extract some significant genes for accurate diagnosis of cancer because not all genes are associated with a cancer. In this paper, we have used representative gene vectors that are highly discriminatory for cancer classes and extracted multiple significant gene subsets based on those representative vectors respectively. Also, an ensemble of neural networks learned from the multiple significant gene subsets is proposed to classify a sample into one of several cancer classes. The performance of the proposed method is systematically evaluated using three different cancer types: Leukemia, colon, and B-cell lymphoma.
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