Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform

  • Cristian Andrés Gallo
  • Jessica Andrea Carballido
  • Ignacio Ponzoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5483)

Abstract

In this paper, a new memetic approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with local search for microarray biclustering is presented. The original features of this proposal are the consideration of opposite regulation and incorporation of a mechanism for tuning the balance between the size and row variance of the biclusters. The approach was developed according to the Platform and Programming Language Independent Interface for Search Algorithms (PISA) framework, thus achieving the possibility of testing and comparing several different memetic MOEAs. The performance of the MOEA strategy based on the SPEA2 performed better, and its resulting biclusters were compared with those obtained by a multi-objective approach recently published. The benchmarks were two datasets corresponding to Saccharomyces cerevisiae and human B-cells Lymphoma. Our proposal achieves a better proportion of coverage of the gene expression data matrix, and it also obtains biclusters with new features that the former existing evolutionary strategies can not detect.

Keywords

Gene regulation biclustering evolutionary algorithms PISA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cristian Andrés Gallo
    • 1
  • Jessica Andrea Carballido
    • 1
  • Ignacio Ponzoni
    • 1
    • 2
  1. 1.Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la ComputaciónUniversidad Nacional del SurBahía BlancaArgentina
  2. 2.Planta Piloto de Ingeniería Química (PLAPIQUI)UNS – CONICET, Complejo CRIBABBBahía BlancaArgentina

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