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This is an application paper of applying classical statistical methods and standard technique of computational intelligence to identify gene diagnostic targets for accurate diagnosis of a medical problem --acute graft-versus-host disease (aGVHD). This is the major complication after allogeneic haematopoietic stem cell transplantation (HSCT), an immunomediated disorder that is driven by allospecific lymphocytes since aGVHD does not occur after autologous stem-cell transplantation. 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 a classifier to detect the aGVHD at on-set of clinical signs. This is a preliminary study and the continuance of our works 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.

Computational Intelligence Methods for Discovering Diagnostic Gene Targets about aGVHD