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首页> 外文期刊>International journal of computational biology and drug design >A gene selection method for classifying cancer samples using 1D discrete wavelet transform.
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A gene selection method for classifying cancer samples using 1D discrete wavelet transform.

机译：一种使用一维离散小波变换对癌症样本进行分类的基因选择方法。

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摘要

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.
机译：为生物样品选择一组判别基因是使用DNA微阵列数据设计高效分类器的重要任务。小波变换是信号处理应用中非常普遍的工具，但其在微阵列基因表达数据分析中的潜力尚待充分探索。在本文中，我们提出了一种基于小波的特征选择方法，该方法将分数分配给基因以区分两类样本。基因表达信号使用几个水平的小波变换分解。选择得分最高的基因以形成用于样品分类的特征集。在这项研究中，特征集与k最近邻（kNN）分类器结合在一起。使用几个实际数据集评估了分类准确性。将它们的性能与几种常用的特征选择方法进行了比较。结果表明，一维小波分析是研究基因表达模式的有价值的工具。

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