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

Yijuan Lu, Qi Tian, Feng Liu, Maribel Sanchez, Yufeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 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.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2006
Subtitle of host publication6th International Conference, Proceedings
PublisherSpringer Verlag
Pages686-693
Number of pages8
ISBN (Print)3540343814, 9783540343813
StatePublished - Jan 1 2006
EventICCS 2006: 6th International Conference on Computational Science - Reading, United Kingdom
Duration: May 28 2006May 31 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3992 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherICCS 2006: 6th International Conference on Computational Science
CountryUnited Kingdom
CityReading
Period5/28/065/31/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lu, Y., Tian, Q., Liu, F., Sanchez, M., & Wang, Y. (2006). A self-supervised learning framework for classifying microarray gene expression data. In Computational Science - ICCS 2006: 6th International Conference, Proceedings (pp. 686-693). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3992 LNCS - II). Springer Verlag.