Exploring novel algorithms for the prediction of cancer classification

In the past decade, DNA microarray technologies have had a great impact on cancer genome research; this technology has been viewed as a promising approach in predicting cancer classes and prognosis outcomes. In this paper, we proposed two systematic methods which can predict cancer classification. By applying the genetic algorithm gene selection (GAGS) method in order to find an optimal information gene subset, we avoid the over-fitting problem caused by attempting to apply a large number of genes to a small number of samples. By extracting significant samples (which we refer to as support vector samples because they are located only on support vectors), we allow the back propagation neural network (BPNN) to learn more tasks. We call this approach the multi-task support vector sample learning (MTSVSL) technique. We demonstrate experimentally that the GAGS and MTSVSL methods yield superior classification performance with application to leukemia and prostate cancer gene expression datasets. Our proposed GAGS and MTSVSL methods are novel approaches which are expedient and perform exceptionally well in cancer diagnosis and clinical outcome prediction.