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 predictiom.
cancer classification multi task learning support vectors back propagation neural networking gene expression profiling genetic algorithm gene selection
Austin H Chen Jen-Chieh Hsu
Department of Medical Informatics. Tzu-Chi University, No. 701, Sec. 3,Jhongyang Rd. Hualien City, H Graduate Institute of Medical Informatics, Tzu-chi University, No. 701, Sec. 3.Jhongyang Rd. Hualien
国际会议
成都
英文
313-318
2010-06-23（万方平台首次上网日期，不代表论文的发表时间）