A new classification model with simple decision rule for discovering optimal feature gene pairs.
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.
Li
Jie
J
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. jieleehz@yahoo.com.cn
Tang
Xianglong
X
D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics; D003110:Colonic Neoplasms / Q000145:classification / Q000235:genetics; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated