Cancer classification using single genes.
Cancer classification using single genes.
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DOI：
10.1142/9781848165632_0017
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Xiaosheng Wang;O. Gotoh
中科院分区：
文献类型：
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作者：
Xiaosheng Wang;O. Gotoh
我们提出了一种基于单基因表达谱的癌症分类方法。根据类对基因的依赖程度，选择类识别能力强的基因。然后，我们建立分类器的基础上的决策规则诱导的单基因选择。我们在三个公开的癌基因表达数据集上测试了我们的单基因分类方法。在大多数情况下，我们只利用一个基因就可以获得相对准确的分类结果。一些与癌症发病机制高度相关的基因被鉴定出来。我们的特征选择和分类方法都是基于粗糙集，一种机器学习方法。与其他方法相比，该方法简单、有效、鲁棒性好。我们的结论是，如果基因选择是合理的，准确的癌症分子分类可以实现非常简单的预测模型的基础上基因表达谱。
We present a method for She classification of cancer based on gene expression profiles using single genes. We select the genes with high class-discrimination capability according to their depended degree by the classes. We then build classifiers based on the decision rules induced by single genes selected. We test our single-gene classification method on three publicly available cancerous gene expression datasets. In a majority of cases, we gain relatively accurate classification outcomes by just utilizing one gene. Some genes highly correlated with the pathogenesis of cancer are identified. Our feature selection and classification approaches are both based on rough sets, a machine learning method. In comparison with other methods, our method is simple, effective and robust. We conclude that, if gene selection is implemented reasonably, accurate molecular classification of cancer can be achieved with very simple predictive models based on gene expression profiles.