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http://dx.doi.org/10.3745/KIPSTD.2008.15-D.6.767

Generating Rank-Comparison Decision Rules with Variable Number of Genes for Cancer Classification  

Yoon, Young-Mi (가천의과학대학교 IT학과)
Bien, Sang-Jay (서울대학교 생물정보학)
Park, Sang-Hyun (연세대학교 컴퓨터과학과)
Publication Information
The KIPS Transactions:PartD / v.15D, no.6, 2008 , pp. 767-776 More about this Journal
Abstract
Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.
Keywords
Data Mining; Classification; Knowledge-Based Data Mining; Microarray Data Analysis; Microarray Data Classification;
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