چکیده
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Objectives:Classification of breast cancer patients into different risk classes is
very important in clinical applications. It is estimated that the advent of highdimensional gene expression data could improve patient classification. In this
study, a new method for transforming the high-dimensional gene expression data
in a low-dimensional space based on wavelet transform (WT) is presented.
Methods:The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a
predictive support vector machine (SVM) model was built upon the reduced
dimensional space. In addition, the proposed method was compared with the
supervised principal component analysis (PCA).
Results:The performance of supervised wavelet and supervised PCA based on
selected genes were better than the signature genes identified in the other
studies. Furthermore, the supervised wavelet method generally performed better
than the supervised PCA for predicting the 5-year survival status of patients with
breast cancer based on microarray data. In addition, the proposed method had a
relatively acceptable performance compared with the other studies.
Conclusion:The results suggest the possibility of developing a new tool using
wavelets for the dimension reduction of microarray data sets in the classification
framework
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