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An Evolutionary Approach for Sample-Based Clustering on Microarray Data

  • Daniel Glez-Peña
  • Fernando Díaz
  • José R. Méndez
  • Juan M. Corchado
  • Florentino Fdez-Riverola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5518)

Abstract

Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coherent from a genetic point of view. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Noise genes are identified and removed from the fuzzy pattern based on their probability of appearance. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema. Experimental results over publicly available microarray data show the effectiveness of the proposed method.

Keywords

simulated annealing sample-based clustering discriminant fuzzy pattern microarray data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Daniel Glez-Peña
    • 1
  • Fernando Díaz
    • 2
  • José R. Méndez
    • 1
  • Juan M. Corchado
    • 3
  • Florentino Fdez-Riverola
    • 1
  1. 1.ESEI: Escuela Superior de Ingeniería InformáticaUniversity of Vigo, Edificio PolitécnicoOurenseSpain
  2. 2.Dept. InformáticaUniversity of Valladolid, Escuela Universitaria de InformáticaSegoviaSpain
  3. 3.Dept. Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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