Analysis of gene expression profiles: class discovery and leaf ordering
Proceedings of the sixth annual international conference on Computational biology
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
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This paper proposes a multi-population χ2 test method for informative gene selection of a tumor from microarray data based on the statistical multi-population χ2 test with the sample data being grouped evenly. To test the effectiveness of the multi-population χ2 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 χ2 test method can 100% correctness rate of diagnosis on colon dataset and 97.1% correctness rate of diagnosis on leukemia dataset, respectively.