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- W1553630105 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." @default.
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- W1553630105 date "2010-01-01" @default.
- W1553630105 modified "2023-09-26" @default.
- W1553630105 title "Machine Learning and Personalized Modeling Based Gene Selection for Acute GvHD Gene Expression Data Analysis" @default.
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- W1553630105 doi "https://doi.org/10.1007/978-3-642-15819-3_29" @default.
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