A convolutional neural network based ensemble method for cancer prediction using DNA methylation data

Cancer is a deadly disease all over the world and its morbidity is increasing at an alarming rate in recent years. With the rapid development of computer science and machine learning technologies, computer-aid cancer prediction has achieved increasingly progress. DNA methylation, as an important epigenetic modification, plays a vital role in the formation and progression of cancer, and therefore can be used as a feature for cancer identification. In this study, we introduce a convolutional neural network based multi-model ensemble method for cancer prediction using DNA methylation data. We first choose five basic machine learning methods as the first stage classifiers and conduct prediction individually. Then, a convolutional neural network is used to find the high-level features among the classifiers and gives a credible prediction result. Experimental results on three DNA methylation datasets of Lung Adenocarcinoma, Liver Hepatocellular Carcinoma and Kidney Clear Cell Carcinoma show the proposed ensemble method can uncover the intricate relationship among the classifiers automatically and achieve better performances.