Experimental design used to study the classifier power of genes lists from different feature selection methods. The most highly ranked genes were selected from 9 gene expression datasets using 11 feature selection approaches (10 methods and random). The power of these gene lists (of length between 2 and 100 genes) to form classifiers was assessed using four supervised classification methods. In each case genes were selected and classifiers trained using a training dataset. They were tested using training and test cross validation. The cumulative relative classifier information (RCI) score was recorded for each classification.
Jeffery et al. BMC Bioinformatics 2006 7:359 doi:10.1186/1471-2105-7-359