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Classifying Cancer Using Partially Correlated Genes Selected by Forward Selection Method  

유시호 (연세대학교 컴퓨터과학과)
조성배 (연세대학교 컴퓨터과학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.41, no.3, 2004 , pp. 83-92 More about this Journal
Abstract
Gene expression profile is numerical data of gene expression level from organism measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify cancer with gene expression profile. Because not all the genes are related to classification, it is needed to select related genes that is called feature selection. This paper proposes a new gene selection method using forward selection method in regression analysis. This method reduces redundant information in the selected genes to have more efficient classification. We used k-nearest neighbor as a classifier and tested with colon cancer dataset. The results are compared with Pearson's coefficient and Spearman's coefficient methods and the proposed method showed better performance. It showed 90.3% accuracy in classification. The method also successfully applied to lymphoma cancer dataset.
Keywords
gene expression profile; feature selection; forward selection method; classification; regression analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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