Feature Selection for SNP Data Based on SVM-RFE and AGA

摘要：

Within the association analysis of high-throughput SNP data, it has been faced two main challenges, the super-high dimension data with less samples and the complex interaction between SNPs of genetic disease. This paper proposes a feature selection method, which combines Support Vector Machine based Recursive Feature Reduction method(SVM-RFE) and Adaptive Genetic Algorithm(AGA). Under the premise of retainning the correlation between SNPs, it significantly reduced the optimization space of critical SNPs using SVM-RFE, and then quickly found the SNPs which distinguish sample type most effectively using AGA. Compared with the Chi-square analysis and modified Relief algorithm, experimental resualt shows that the suspect SNPs selected by this method has a better ability to distinguish the type of samples. This method provides a feasible way for SNPs association study, and screen out an appropriate scale of SNP set for further biomedical research.

关键词： SVM SVM-RFE GA SNP feature select

作者: Xutao Yang Yue Wu Min Jia Zhou Lei Zongtian Liu

作者单位: Department of Computer Engineering and Science, Shanghai University, Shanghai, China

会议类型: 国际会议

会议名称: 2010 International Conference on Circuit and Signal Processing(2010年电路与信号处理国际会议 ICCSP 2010)

会议地点: 上海

会议语种:英文

页码: 201-205

在线出版日期: 2010-12-25（万方平台首次上网日期，不代表论文的发表时间）