As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
In this paper a novel gene selection method based on personalized modeling is presented 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. In this work we have provided a comparative study using the proposed Personalized Modeling based Gene Selection method (PMGS) on the GvHD Macroarray dataset collected. This is the first study which utilises both computational and biological evidence for the involvement of a limited number of genes for the diagnosis of aGVHD and the use of a personalized modeling for the analysis of this disease. Directions for further studies are also outlined.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.