Gene selection for cancer classification using bootstrapped 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. In this paper, we propose a novel system for selecting a set of genes for cancer classification. This system is based on a linear support vector machine and a genetic algorithm. To overcome the problem of the small size of training samples, bootstrap methods are combined into genetic search. Two databases are considered: the colon cancer database and the leukemia database. Our experimental results show that the proposed method is capable of finding genes that discriminate between normal cells and cancer cells and generalizes well.