Abstract
This paper proposes a multi-population χ 2 test method for informative gene selection of a tumor from microarray data based on the statistical multi-population χ 2 test with the sample data being grouped evenly. To test the effectiveness of the multi-population χ 2 test method, we use the support vector machine (SVM) to construct a tumor diagnosis system (i.e., a binary classifier) based on the identified informative genes on the colon and leukemia data. It is shown by the experiments that the constructed diagnosis system with the multi-population χ 2 test method can 100% correctness rate of diagnosis on colon dataset and 97.1% correctness rate of diagnosis on leukemia dataset, respectively.
This work was supported by the Natural Science Foundation of China for Project 60471054
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Luo, J., Ma, J. (2005). A Multi-population χ 2 Test Approach to Informative Gene Selection. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_53
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DOI: https://doi.org/10.1007/11508069_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
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