NDLI: A rank sum test method for informative gene discovery

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A rank sum test method for informative gene discovery

Content Provider	ACM Digital Library
Author	Ma, Jinwen Pei, Jian Lee, Dik Lun Deng, Lin
Abstract	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 cannot 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.
Starting Page	410
Ending Page	419
Page Count	10
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014099
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Classification Ranking criterion Support vector machine Informative genes Rank sum test
Content Type	Text
Resource Type	Article

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