Analysis of Gene Sensitivity for Tumor Informative Genes Selection Based on SVM
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摘要: 提出基于支持向量机的灵敏度分析方法选取结肠癌特征基因.用支持向量机分析基因对分类决策函数的灵敏度,递归去除灵敏度较低的若干基因,得到一组候选特征基因子集;以支持向量机为分类工具,检验候选特征基因子集对样本分类的贡献,选取具有最佳分类能力的候选特征基因子集作为结肠癌特征基因子集.通过实验比较,该特征基因子集的分类能力优于文献给出的其他特征基因子集,表明了该方法的可行性和有效性.Abstract: In this paper we proposed an approach for tumor informative genes selection by analysis of gene sen- sitivity based on SVM.We analyzed the gene expression profiles of colon and recursively eliminated the genes which have lower sensitivity to SVM,then a set of candidate nested feature subsets were generated.Support Vector Machines were employed to classify the samples using these candidate feature subsets,and the feature subset with a minimum error was chosen as a set of colon informative genes.The results show that this feature subset contains more tumor classification information than other feature subsets identified in the literatures. The method proposed in this paper is feasible and effective.
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Keywords:
- feature selection /
- support vector machine /
- gene expression profile /
- sensitivity
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