Machine learning and personalized modeling based gene selection for acute GvHD gene expression data analysis

Authors:
Maurizio Fiasché;Maria Cuzzola;Roberta Fedele;Pasquale Iacopino;Francesco C. Morabito
Affiliations:
DIMET, University "Mediterranea" of Reggio Calabria, Italy and Transplant Regional Center of Stem Cells and Cellular Therapy, "A. Neri", Reggio Calabria, Italy;Transplant Regional Center of Stem Cells and Cellular Therapy, "A. Neri", Reggio Calabria, Italy;Transplant Regional Center of Stem Cells and Cellular Therapy, "A. Neri", Reggio Calabria, Italy;Transplant Regional Center of Stem Cells and Cellular Therapy, "A. Neri", Reggio Calabria, Italy;DIMET, University "Mediterranea" of Reggio Calabria, Italy
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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Abstract

In this paper a novel gene selection method based on personalized modeling is proposed and is compared with classical machine learning techniques to identify diagnostic gene targets and to use them for a successful diagnosis of a medical problem - acute graft-versus-host disease (aGvHD). An analysis using the integrated approach of new data with the existing models is evaluated. Identifying a compact set of genes from gene expression data is a critical step in bioinformatics research. Personalized modeling is a recently introduced technique for constructing clinical decision support systems. This is a novel study which utilises both computational and biological evidence and the use of a personalized modeling for the analysis of this disease. Directions for further studies are also outlined.