Finding expressed genes using genetic algorithms and support vector machines

The gene expression data obtained from microarrays have shown useful in cancer classification. DNA microarray data have extremely high dimensionality compared to the small number of available samples. An important step in microarray studies is to remove genes irrelevant to the learning problem and to select a small number of genes expressed in biological samples under specific conditions. We propose a novel feature subset selection algorithm to identify expressed genes for cancer classification. This algorithm is based on genetic algorithms and support vector machine algorithms. This new algorithm is very efficient for selecting sets of genes in very high dimensional feature space. Two databases are considered: the colon cancer database and the leukemia database. Our experimental results show the effectiveness of the proposed algorithms.