LS Bound based gene selection for DNA microarray data

• DOI: 10.1093/bioinformatics/bti216
• Publication Date: 2004-12-15T02:58:40Z
• Authors: X. Zhou; K. Z. Mao
• Keywords: Gene Expression Regulation, Neoplastic; Models, Statistical; Models, Genetic; Gene Expression Profiling; Neoplasms; 0206 medical engineering; Biomarkers, Tumor; Discriminant Analysis; Humans; 02 engineering and technology; Neoplasm Proteins; Oligonucleotide Array Sequence Analysis

One problem with discriminant analysis of DNA microarray data is that each sample is represented by quite a large number of genes, and many of them are irrelevant, insignificant or redundant to the discriminant problem at hand. Methods for selecting important genes are, therefore, of much significance in microarray data analysis. In the present study, a new criterion, called LS Bound measure, is proposed to address the gene selection problem. The LS Bound measure is derived from leave-one-out procedure of LS-SVMs (least squares support vector machines), and as the upper bound for leave-one-out classification results it reflects to some extent the generalization performance of gene subsets.We applied this LS Bound measure for gene selection on two benchmark microarray datasets: colon cancer and leukemia. We also compared the LS Bound measure with other evaluation criteria, including the well-known Fisher's ratio and Mahalanobis class separability measure, and other published gene selection algorithms, including Weighting factor and SVM Recursive Feature Elimination. The strength of the LS Bound measure is that it provides gene subsets leading to more accurate classification results than the filter method while its computational complexity is at the level of the filter method.A companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/. The website contains: (1) the source code of the gene selection algorithm; (2) the complete set of tables and figures regarding the experimental study; (3) proof of the inequality (9)[e-mail].