document: A rank sum test method for informative gene discovery Finding informative genes from microarray data is an important research problem in bioinformatics research and applications . Most of the existing methods rank features according to their discriminative capability and then find a subset of discriminative genes ( usually top k genes ) . In particular , t-statistic criterion and its variants have been adopted extensively . This kind of methods rely on the statistics principle of t-test , which requires that the data follows a normal distribution . However , according to our investigation , the normality condition often can not be met in real data sets . To avoid the assumption of the normality condition , in this paper , we propose a rank sum test method for informative gene discovery . The method uses a rank-sum statistic as the ranking criterion . Moreover , we propose using the significance level threshold , instead of the number of informative genes , as the parameter . The significance level threshold as a parameter carries the quality specification in statistics . We follow the Pitman efficiency theory to show that the rank sum method is more accurate and more robust than the t-statistic method in theory . To verify the effectiveness of the rank sum method , we use support vector machine ( SVM ) to construct classifiers based on the identified informative genes on two well known data sets , namely colon data and leukemia data . The prediction accuracy reaches 96.2 % on the colon data and 100 % on the leukemia data . The results are clearly better than those from the previous feature ranking methods . By experiments , we also verify that using significance level threshold is more effective than directly specifying an arbitrary k.
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