USVM: Selection of SNPs in Diseases Association Study Using UMDA and SVM
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection has become a necessity in many bioinformatics applications. In this paper, we propose a wrapper algorithm named USVM which combines Univariate Marginal Distribution Algorithm (UMDA) and Support Vector Machine (SVM) for disease association study. USVM not only eliminates the redundancy of feature, but also solves the problem of SVMs parameters selection. We use USVM to analyze the Crohns disease (CD) dataset including 387 samples and each one has 103 SNPs. The experimental results show that our algorithm outperforms the current algorithms including DNF, CSP, ORF and so on.
Bin Wei Qinke Peng Jing Li Xuejiao Kan Chenyao Li
Systems Engineering Institute of Electronic and Information Engineering School, Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University Shaanxi, China
国际会议
成都
英文
1-5
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）