Disease Prediction Using Ranks of Gene Expressions

A large number of studies have been performed to iden-tify biomarkers that will allow efficient detection and de-termination 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 bio-marker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different ex-perimental conditions. Because of this difficulty in ex-perimental standardization of microarray data, it is cur-rently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a meth-od 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 ex-perimental conditions