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Rough Sets in Ortholog Gene Detection

Selection of Feature Subsets and Case Reduction Considering Imbalance
  • Deborah Galpert Cańizares
  • Reinier Millo Sánchez
  • María Matilde García Lorenzo
  • Gladys Casas Cardoso
  • Ricardo Grau Abalo
  • Leticia Arco García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)

Abstract

Ortholog detection should be improved because of the real value of ortholog genes in the prediction of protein functions. Datasets in the binary classification problem can be represented as information systems. We use a gene pair extended similarity relation based on an extension of the Rough Set Theory and aggregated gene similarity measures as gene features, to select feature subsets with the aid of quality measures that take imbalance into account. The proposed procedure can be useful for datasets with few features and discrete parameters. The case reduction obtained from the approximation of ortholog and non-ortholog concepts might be an effective method to cope with extremely high imbalance in supervised classification.

Keywords

Ortholog Detection Rough Sets Classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Deborah Galpert Cańizares
    • 1
  • Reinier Millo Sánchez
    • 1
  • María Matilde García Lorenzo
    • 1
  • Gladys Casas Cardoso
    • 1
  • Ricardo Grau Abalo
    • 1
  • Leticia Arco García
    • 1
  1. 1.Computer Science DepartmentUniversidad Central “Marta Abreu” de Las VillasSanta ClaraCuba

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