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Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data

  • Maurizio Fiasché
  • Anju Verma
  • Maria Cuzzola
  • Pasquale Iacopino
  • Nikola Kasabov
  • Francesco C. Morabito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5769)

Abstract

This is an application paper of applying standard methods of computational intelligence to identify gene diagnostic targets and to use them for a successful diagnosis of a medical problem - acute graft-versus-host disease (aGVHD). This is the major complication after allogeneic haematopoietic stem cell transplantation (HSCT) in which functional immune cells of donor recognize the recipient as “foreign” and mount an immunologic attack. In this paper we analyzed gene-expression profiles of 47 genes associated with allo-reactivity in 59 patients submitted to HSCT. We have applied 2 feature selection algorithms combined with 2 different classifiers to detect the aGVHD at on-set of clinical signs. This is a preliminary study and the first paper which tackles both computational and biological evidence for the involvement of a limited number of genes for diagnosis of aGVHD. Directions for further studies are outlined.

Keywords

Neural Networks Feature Selection GEP GVHD Gene selection Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maurizio Fiasché
    • 1
  • Anju Verma
    • 2
  • Maria Cuzzola
    • 3
  • Pasquale Iacopino
    • 3
  • Nikola Kasabov
    • 2
  • Francesco C. Morabito
    • 1
  1. 1.University “Mediterranea” of Reggio Calabria, DIMETReggio CalabriaItaly
  2. 2.KEDRI, Auckland University of TechnologyAucklandNew Zealand
  3. 3.Regional Center of Stem Cells and Cellular Therapy, “A. Neri”Reggio CalabriaItaly

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