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Disease Prediction Using Ranks of Gene Expressions

 Ki-Yeol Kim  ;  Dong Hyuk Ki  ;  Hyun Cheol Chung  ;  Sun Young Rha 
 Genomics & Informatics, Vol.6(3) : 136-141, 2008 
Journal Title
 Genomics & Informatics 
Issue Date
A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient's disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.
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1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실)
1. Journal Papers (연구논문) > 5. Research Institutes (연구소) > Oral Cancer Research Institute (구강종양연구소)
Yonsei Authors
김기열(Kim, Ki Yeol) ORCID logo https://orcid.org/0000-0001-5357-1067
라선영(Rha, Sun Young)
정현철(Chung, Hyun Cheol) ORCID logo https://orcid.org/0000-0002-0920-9471
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