Jose Adarsh, Mugler Dale, Duan Zhong-Hui
Department of Biomedical Engineering, University of Akron, Akron, OH 44236, USA.
Int J Comput Biol Drug Des. 2009;2(4):398-411. doi: 10.1504/IJCBDD.2009.030769. Epub 2009 Jan 4.
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.
为生物样本选择一组判别基因是利用DNA微阵列数据设计高效分类器的一项重要任务。小波变换是信号处理应用中非常常用的工具，但其在微阵列基因表达数据分析中的潜力尚未得到充分探索。在本文中，我们提出了一种基于小波的特征选择方法，该方法为基因分配分数以区分两类样本。基因表达信号通过小波变换的多个层次进行分解。选择得分最高的基因以形成用于样本分类的特征集。在本研究中，特征集与k近邻（kNN）分类器相结合。使用几个真实数据集评估分类准确率。将它们的性能与几种常用的特征选择方法进行比较。结果表明，一维小波分析是研究基因表达模式的有价值工具。
