A new classification model with simple decision rule for discovering optimal feature gene pairs

Authors:
Jie Li;Xianglong Tang
Affiliations:
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

Classifiers have been widely used to select an optimal subset of feature genes from microarray data for accurate classification of cancer samples and cancer-related studies. However, the classification rules derived from most classifiers are complex and difficult to understand in biological significance. How to solve this problem is a new challenge. In this paper, a new classification model based on gene pair is proposed to address the problem. The experimental results on several microarray data demonstrate that the proposed classification model performs well in finding a large number of excellent feature gene pairs. A 100% LOOCV classification accuracy can be achieved using a single classification model based on optimal feature gene pair or combining multiple top-ranked classification models. Using the proposed method, we successfully identified important cancer-related genes that had been validated in previous biological studies while they were not discovered by the other methods.