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MOACO Biclustering of gene expression data

  • Junwan Liu*
  • , Zhoujun Li
  • , Xiaohua Hu
  • , Yiming Chen
  • *Corresponding author for this work
  • Central South University of Forestry & Technology
  • Drexel University
  • National University of Defense Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Many bioinformatics data sets come from DNA microarray experiments. Biclustering of gene expression data can identify genes with similar behaviour with respect to different conditions. Ant Colony Optimisation (ACO) algorithms have been shown to be effective problem solving strategies for a wide range of problem domains. Multiple Objective Ant Colony Optimisation (MOACO) mainly focuses on solving the multiple objective combinatorial optimisation problems. This paper incorporates crowding update technology into MOACOB and proposes crowding MOACO biclustering algorithm to mine biclusters from gene expression data. Experimental results are shown for biclustering algorithm on two real gene expression data.

Original languageEnglish
Pages (from-to)58-72
Number of pages15
JournalInternational Journal of Functional Informatics and Personalised Medicine
Volume3
Issue number1
DOIs
StatePublished - 2010

Keywords

  • ACO
  • Ant colony optimisation
  • Biclustering
  • Bioinformatics
  • DNA microarray
  • Gene expression data
  • Multiple objective

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