genEnsemble: A new model for the combination of classifiers and integration of biological knowledge applied to genomic data

  • Authors:

  • Miguel Reboiro-Jato;RosalíA Laza;Hugo LóPez-FernáNdez;Daniel Glez-PeñA;Fernando DíAz;Florentino Fdez-Riverola

  • Affiliations:

  • Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, University of Vigo, 32004 Ourense, Spain;Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, University of Vigo, 32004 Ourense, Spain;Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, University of Vigo, 32004 Ourense, Spain;Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, University of Vigo, 32004 Ourense, Spain;Escuela Universitaria de Informática, Plaza de Santa Eulalia 9-11, University of Valladolid, 40005 Segovia, Spain;Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, University of Vigo, 32004 Ourense, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

In the last years, microarray technology has become widely used in relevant biomedical areas such as drug target identification, pharmacogenomics or clinical research. However, the necessary prerequisites for the development of valuable translational microarray-based diagnostic tools are (i) a solid understanding of the relative strengths and weaknesses of underlying classification methods and (ii) a biologically plausible and understandable behaviour of such models from a biological point of view. In this paper we propose a novel classifier able to combine the advantages of ensemble approaches with the benefits obtained from the true integration of biological knowledge in the classification process of different microarray samples. The aim of the current work is to guarantee the robustness of the proposed classification model when applied to several microarray data in an inter-dataset scenario. The comparative experimental results demonstrated that our proposal working with biological knowledge outperforms other well-known simple classifiers and ensemble alternatives in binary and multiclass cancer prediction problems using publicly available data.