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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lu, Y., Tian, Q., Liu, F., Sanchez, M., & Wang, Y. (2006). A self-supervised learning framework for classifying microarray gene expression data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3992 LNCS-II, pp. 686–693). Springer Verlag. https://doi.org/10.1007/11758525_93
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