Disease Prediction Using Ranks of Gene Expressions
Genomics & Informatics
;
: 136-141, 2008.
Artigo
em Inglês
| WPRIM
| ID: wpr-22935
ABSTRACT
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Biomarcadores
/
Viés
/
Expressão Gênica
/
Neoplasias do Colo
/
Análise de Sequência com Séries de Oligonucleotídeos
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Inglês
Revista:
Genomics & Informatics
Ano de publicação:
2008
Tipo de documento:
Artigo
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