Colon cancer prediction with genetics profiles using evolutionary techniques

Microarray data provides information on gene expression levels of thousands of genes in a cell in a single experiment. DNA microarray is a powerful tool in the diagnosis of cancer. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In this study comparison between class prediction accuracy of two different classifiers, Genetic Programming and Genetically Evolved Decision Trees, was carried out using the best 10 and best 20 genes ranked by the t-statistic and mutual information. Genetic Programming proved out to be the better classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy using mutual information based feature selection. We conclude that Genetic Programming together with mutual information based feature selection is the most efficient alternative to the existing colon cancer prediction techniques.