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首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique
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Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

机译：使用微阵列基因表达数据集检测白血病和小圆形蓝细胞肿瘤癌：结合数据降维和变量选择技术

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摘要

Using gene expression data in cancer classification plays an important role for solving the fundamental problems relating to cancer diagnosis. Because of high throughput of gene expression data for healthy and patient samples, a variable selection method can be applied to reduce complexity of the model and improve the classification performance. Since variable selection procedures pose a risk of over-fitting, when a large number of variables with respect to sample are used, we have proposed a method for coupling data dimension reduction and variable selection in the present study. This approach uses the concept of variable clustering for the original data set. Significant components of local principal component analysis models have just been retained from all clusters. Then, the variable selection algorithm is performed on these locally derived principal component variables. The proposed algorithm has been evaluated on two gene expression data sets; namely, acute Leukemia and small round blue-cell tumor (SRBCT). Our results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire microarray gene expression profile.
机译：在癌症分类中使用基因表达数据对于解决与癌症诊断有关的基本问题具有重要作用。由于健康和患者样品的基因表达数据的通量高，因此可以应用变量选择方法来降低模型的复杂性并提高分类性能。由于变量选择程序存在过度拟合的风险，因此当使用相对于样本的大量变量时，我们在本研究中提出了一种将数据降维与变量选择耦合的方法。这种方法对原始数据集使用变量聚类的概念。本地主成分分析模型的重要成分刚刚从所有群集中保留下来。然后，对这些局部派生的主成分变量执行变量选择算法。该算法已经在两个基因表达数据集上进行了评估。即急性白血病和小圆形蓝细胞肿瘤（SRBCT）。我们的结果证实，在减少的数据上获得的分类模型比在整个微阵列基因表达谱上获得的分类模型更好。

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