A Probabilistic mechanism based on clustering analysis and distance measure for subset gene selection

Tzu-Tsung Wong, Kuan Liang Liu

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Many subset gene selection methods for microarray data employ classification tools to evaluate the discernability of a gene subset on a specific disease, and this evaluation process generally has a high computational complexity. In this study, we propose a probabilistic mechanism supported by a density-based clustering method and a distance measure to perform individual and group gene replacement for gene selection. Analysts can choose proper values for the parameters of the probabilistic mechanism to set the computational complexity for gene selection. The discernability of a gene subset on classification is evaluated by the distance measure to avoid the language bias that can be introduced by classification tools. Our experimental results on six microarray data sets show that the probabilistic mechanism can effectively and efficiently filter a gene subset with a high discernability on cancer diagnosis.

Original languageEnglish
Pages (from-to)2144-2149
Number of pages6
JournalExpert Systems With Applications
Volume37
Issue number3
DOIs
Publication statusPublished - 2010 Mar 15

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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