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.
In this paper we proposed an approach for tumor informative genes selection by analysis of gene sensitivity 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
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2007年第9期954-958,共5页
Journal of Beijing University of Technology
基金
国家自然科学基金重点资助项目(60234020)
安徽省教育厅科研项目(KJ2007B001).
关键词
特征选取
支持向量机
基因表达谱
灵敏度
feature selection
support vector machine
gene expression profile
sensitivity
