%0 Journal Article
%T Combined SNP feature selection based on relief and SVM-RFE<br>基于Relief和SVM-RFE的组合式SNP特征选择
%A WU Hong-xi
%A WU Yue
%A LIU Zong-tian
%A LEI Zhou
%A <br>吴红霞
%A 吴　悦
%A 刘宗田
%A 雷　州
%J 计算机应用研究
%D 2012
%I 
%X The genome-wide association study (GWAS)on SNPs faces two big issues: high dimensional SNP data with small sample characteristics and complex mechanisms of genetic diseases. This paper proposed a combined SNP feature selection method through bring feature selection methods into GWAS. The method included two stages: filter stage, it used Relief algorithm to eliminate irrelevant SNP features, wrapper stage, it used support vector machine based recursive feature reduction (SVM-RFE) algorithm to select the key SNPs set. Experiments show that the proposed method has an obviously better performance than SVM RFE algorithm, and also gains higher classification accuracy than Relief-SVM algorithm, which provides an effective way for SNP genome-wide association analysis.
%K SNP
%K GWAS
%K feature selection
%K filter
%K wrapper
%K combined<br>单核苷酸多态性
%K 全基因组关联研究
%K 特征选择
%K 过滤式
%K 缠绕式
%K 组合式
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=AB480754FB5F26FED71400F02BC49581&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=2C44BAF8BCD26B57&eid=736738473FF7E908&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=15