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Rank-based Multiclass Gene Selection for Cancer Classification with Naive Bayes Classifiers based on Gene Expression Profiles  

Hong, Jin-Hyuk (연세대학교 컴퓨터과학과)
Cho, Sung-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Computer Systems and Theory / v.35, no.8, 2008 , pp. 372-377 More about this Journal
Abstract
Multiclass cancer classification has been actively investigated based on gene expression profiles, where it determines the type of cancer by analyzing the large amount of gene expression data collected by the DNA microarray technology. Since gene expression data include many genes not related to a target cancer, it is required to select informative genes in order to obtain highly accurate classification. Conventional rank-based gene selection methods often use ideal marker genes basically devised for binary classification, so it is difficult to directly apply them to multiclass classification. In this paper, we propose a novel method for multiclass gene selection, which does not use ideal marker genes but directly analyzes the distribution of gene expression. It measures the class-discriminability by discretizing gene expression levels into several regions and analyzing the frequency of training samples for each region, and then classifies samples by using the naive Bayes classifier. We have demonstrated the usefulness of the proposed method for various representative benchmark datasets of multiclass cancer classification.
Keywords
gene expression profiles; multiclass cancer classification; gene selection;
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