New Entropy-Based Method for Gene Selection

作者：Mahmoodian Hamid^*; Marhaban M H; Rahim R Abdul; Rosli R; Saripan I

来源：IETE Journal of Research, 2009, 55(4): 162-168.

DOI：10.4103/0377-2063.55985

• Gene selection
• Penalized logistic regression
• Shannon theory

Dimension reduction and selection of a small number of genes with high ability, to discriminate objects, are -important challenges in micro-array data analysis. Gene selection, based on top ranked genes which individually have high power to discriminate objects, is a traditional method that doesnt consider the redundancy among the genes. Some results present that subset of genes with low degree of redundancy can show a more comprehensive representation of the targeted classes than one with redundant genes. In this paper, we use Shannon theorem and penalized logistic regression (PLR) as a probability estimator to present a new algorithm for dimension reduction and collect a subset of representative genes of gene expression profile. Breast cancer, leukemia, colon and lung datasets have been -classified based on proposed gene selection algorithm by PLR classifier. In most cases the results show a good performance compared to other recent researches.

• 出版日期2009-8