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Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression Data

  • Ons Maâtouk
  • Wassim Ayadi
  • Hend Bouziri
  • Beatrice Duval
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8626)

Abstract

Microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions.

This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.

Keywords

Biclustering Evolutionary algorithm Crossover method Microarray data Data mining 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ons Maâtouk
    • 1
    • 2
  • Wassim Ayadi
    • 2
    • 3
  • Hend Bouziri
    • 1
  • Beatrice Duval
    • 2
  1. 1.LARODEC Laboratory, ISG TunisUniversité de TunisTunisia
  2. 2.LERIAUniversité d’AngersAngersFrance
  3. 3.LaTICE Laboratory, ESSTTUniversité de TunisTunisia

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