Application of wavelet-based neural network on DNA microarray data |
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Authors: | Jack Lee Benny Zee |
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Institution: | Centre for Clinical Trials, School of Public Health; Department of Clinical Oncology, the Chinese University of Hong Kong, Hong Kong SAR |
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Abstract: | The advantage of using DNA microarray data when investigating human cancer gene expressions is its ability to generate
enormous amount of information from a single assay in order to speed up the scientific evaluation process. The number of
variables from the gene expression data coupled with comparably much less number of samples creates new challenges to
scientists and statisticians. In particular, the problems include enormous degree of collinearity among genes expressions,
likely violation of model assumptions as well as high level of noise with potential outliers. To deal with these problems,
we propose a block wavelet shrinkage principal component (BWSPCA) analysis method to optimize the information during the
noise reduction process. This paper firstly uses the National Cancer Institute database (NC160) as an illustration and shows
a significant improvement in dimension reduction. Secondly we combine BWSPCA with an artificial neural network-based gene
minimization strategy to establish a Block Wavelet-based Neural Network model in a robust and accurate cancer classification
process (BWNN). Our extensive experiments on six public cancer datasets have shown that the method of BWNN for tumor
classification performed well, especially on some difficult instances with large-class (more than two) expression data. This
proposed method is extremely useful for data denoising and is competitiveness with respect to other methods such as BagBoost,
RandomForest (RanFor), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). |
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Keywords: | wavelet shrinkage denoising ANN classification of cancer types |
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