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.
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Fiasché, M., Verma, A., Cuzzola, M., Iacopino, P., Kasabov, N., Morabito, F.C. (2009). Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_2
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DOI: https://doi.org/10.1007/978-3-642-04277-5_2
Publisher Name: Springer, Berlin, Heidelberg
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