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

  • Authors:

  • Yijuan Lu;Qi Tian;Feng Liu;Maribel Sanchez;Yufeng Wang

  • Affiliations:

  • Department of Computer Science, University of Texas at San Antonio, TX;Department of Computer Science, University of Texas at San Antonio, TX;Department of Pharmacology, University of Texas Health Science Center, at San Antonio, TX;Department of Biology, University of Texas at San Antonio, TX;Department of Biology, University of Texas at San Antonio, TX

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
  • Year:
  • 2006

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Abstract

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 framework 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.