Gene selection for leukemia subtype classification from gene expression profile

It is very important but difficult to identify which genes in gene expression data can contribute most to tumor subtype classification. An approach to select a small subset of genes for leukemia subtype classification from large scale gene expression profile is proposed in this paper. Having removed the noisy genes with little relevance to the classification task, the "sequential floating forward search" method was employed to generate candidate feature subsets consisting of informative genes, and then, a support vector machine was employed as a classifier to select the optimal feature subset with minimum classification errors. The results of our experiment showed that all the samples can be correctly classified without any error with only five genes.