Optimal search-based gene selection for cancer prognosis

Jason J. Li, Hua Su, Hsinchun Chen

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

Abstract

Gene array data have been widely used for cancer diagnosis in recent years. However, high dimensionality has been a major problem for gene array-based classification. Gene selection is critical for accurate classification and for identifying the marker genes to discriminate different tumor types. This paper created a framework of gene selection methods based on previous studies. We focused on optimal search-based gene subset selection methods that evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this study is the first to introduce tabu search to gene selection from high dimensional gene array data. Experimental studies on several gene array datasets demonstrated the effectiveness of optimal search-based gene subset selection to identify marker genes.

Original languageEnglish (US)
Title of host publicationAssociation for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale
Pages2672-2679
Number of pages8
Volume6
StatePublished - 2005
Event11th Americas Conference on Information Systems, AMCIS 2005 - Omaha, NE, United States
Duration: Aug 11 2005Aug 15 2005

Other

Other11th Americas Conference on Information Systems, AMCIS 2005
CountryUnited States
CityOmaha, NE
Period8/11/058/15/05

Fingerprint

cancer
Genes
tabu
performance
Tabu search
Group
Tumors

Keywords

  • Cancer prognosis
  • Feature selection
  • Optimal search
  • Tabu search

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems
  • Library and Information Sciences

Cite this

Li, J. J., Su, H., & Chen, H. (2005). Optimal search-based gene selection for cancer prognosis. In Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale (Vol. 6, pp. 2672-2679)

Optimal search-based gene selection for cancer prognosis. / Li, Jason J.; Su, Hua; Chen, Hsinchun.

Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale. Vol. 6 2005. p. 2672-2679.

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

Li, JJ, Su, H & Chen, H 2005, Optimal search-based gene selection for cancer prognosis. in Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale. vol. 6, pp. 2672-2679, 11th Americas Conference on Information Systems, AMCIS 2005, Omaha, NE, United States, 8/11/05.
Li JJ, Su H, Chen H. Optimal search-based gene selection for cancer prognosis. In Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale. Vol. 6. 2005. p. 2672-2679
Li, Jason J. ; Su, Hua ; Chen, Hsinchun. / Optimal search-based gene selection for cancer prognosis. Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale. Vol. 6 2005. pp. 2672-2679
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