Goodness-of-fit techniques
Selecting features in microarray classification using ROC curves
Pattern Recognition
Feature cluster selection for high-throughput data analysis
International Journal of Data Mining and Bioinformatics
Matrix factorisation methods applied in microarray data analysis
International Journal of Data Mining and Bioinformatics
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We address the problem of ranking differentially expressed genes in high throughput experiments using Receiver Operating Characteristic ROC curves. As it is generally unknown whether large expression values constitute 'positive' or 'negative' results or which group is 'healthy' or 'diseased', we generate four ROC curves per gene. We then consider classification indices based on all or part of the four ROC curves and identify genes ranked low by the area under the curve AUC but high by at least one alternative index, invariably resulting to the discovery of genes that would otherwise be missed by the AUC index.