Selection of the most useful subset of genes for gene expression-based classification

Recently, there has been a growing interest in classification of patient samples based on gene expressions. Here the classification task is made more difficult by the noisy nature of the data, and by the overwhelming number of genes relative to the number of available training samples in the data set. Moreover, many of these genes are irrelevant for classification and have negative effect on the accuracy and on the required learning time for the classifier. We propose a new evolutionary computation method to select the most useful subset of genes for molecular classification. We apply this method to three benchmark data sets and present our unbiased experimental results.