A self-supervised learning framework for classifying microarray gene expression data

It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning frame-work for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.