DWT based Feature Extraction of Gene Expression Data for Tissue Classification

X. Dong, G. Sun, and G. Xu (PRC)


discrete wavelet transform, feature extraction, gene expression, tissue classification


DNA microarrays can be used to measure the expression levels of thousands of genes simultaneously and so it is usually an effective tool of cancer research. A discrete wavelet transform(DWT) based feature extraction method for tissue classification was introduced, by which microarray expression data are transformed into time-scale domain and used as input of a tissue classifier. Finally, some test and comparison experiments for the feature extraction method have been made by using the weighted voting classification scheme[1] . Experiment results show that the correct rate is at least 90% in tumor vs. normal classification by using the feature extraction method.

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