Personalized Modeling based Gene Selection for acute GvHD Gene Expression Data Analysis: a Computational Framework Proposed

摘要

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. The aGvHD 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. 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.