A neural network model for early diagnosis of acute GVHD based on gene expression data

Author

Fiasché, M. ; Cuzzola, M. ; Cacciola, M. ; Megali, G. ; Fedele, R. ; Iacopino, P. ; Morabito, F.C.

Author_Institution

DIMET, Univ. Mediterranea of Reggio Calabria, Feo di Vito, Italy

fYear

2009

fDate

17-21 May 2009

Firstpage

1

Lastpage

4

Abstract

Acute graft-versus-host disease (aGVHD) is the major complication after allogeneic haematopoietic stem cell transplantation (HSCT) in which functional immune cells of donor recognize the recipient as ldquoforeignrdquo and mount an immunologic attack. In this paper we analyzed gene-expression profiles of 47 genes associated with alloreactivity in 59 patients submitted to HSCT. We have applied a dimension reduction technique to found the most important subset of genes to make a diagnosis of aGVHD. The composed subset has been used in order to train and test a suitable artificial neural network (ANN) to detect the aGVHD at on-set of clinical signs.

Keywords

cellular biophysics; diseases; genetics; medical computing; neural nets; patient diagnosis; acute graft-versus-host disease early diagnosis; alloreactivity; artificial neural network model; dimension reduction technique; gene expression data; haematopoietic stem cell transplantation; Artificial neural networks; Bioinformatics; Diseases; Gene expression; Humans; Immune system; Medical treatment; Neural networks; Stem cells; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop on

Conference_Location

Minneapolis, MN

Print_ISBN

978-1-4244-4761-9

Electronic_ISBN

978-1-4244-4762-6

Type

conf

DOI

10.1109/GENSIPS.2009.5174360

Filename

5174360