Authors: Adarsh Jose, Dale Mugler, Zhong-Hui Duan
Addresses: Department of Biomedical Engineering, University of Akron, Akron, OH 44236, USA. ' Department of Biomedical Engineering, University of Akron, Akron, OH 44236, USA. ' Integrated Bioscience Program, Department of Computer Science, University of Akron, Akron, OH 44236, USA
Abstract: Selecting a set of discriminant genes for biological samples is an important task for designing highly efficient classifiers using DNA microarray data. The wavelet transform is a very common tool in signal-processing applications, but its potential in the analysis of microarray gene expression data is yet to be explored fully. In this paper, we present a wavelet-based feature selection method that assigns scores to genes for differentiating samples between two classes. The gene expression signal is decomposed using several levels of the wavelet transform. The genes with the highest scores are selected to form a feature set for sample classification. In this study, the feature sets were coupled with k-nearest neighbour (kNN) classifiers. The classification accuracies were assessed using several real data sets. Their performances were compared with several commonly used feature selection methods. The results demonstrate that 1D wavelet analysis is a valuable tool for studying gene expression patterns.
Keywords: DNA microarrays; gene expression profiles; feature selection; wavelet applications; biological sample classification; gene selection; cancer sample classification; cancer samples; 1D discrete wavelet transforms; discriminant genes; gene expression patterns.
International Journal of Computational Biology and Drug Design, 2009 Vol.2 No.4, pp.398 - 411
Published online: 04 Jan 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article