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编号:13126113
基于随机森林算法的川芎成分—靶点—疾病网络的预测研究(4)
http://www.100md.com 2014年6月15日 《中国中药杂志》 2014年第12期
     (School of Traditional Chinese Medicine,Guangdong Pharmaceutical University,Guangzhou 510006,China)

    [Abstract] To collect small molecule drugs and their drug target data such as enzymes,ion channels,G-protein-coupled receptors and nuclear receptors from KEGG database as the training sets,in order to establish drug-target interaction models based on the random forest algorithm. The accuracies of the models were evaluated by the 10-fold cross-validation test,showing that the predicted success rates of the four drug target models were 71.34%,67.08%,73.17% and 67.83%,respectively. The models were adopted to predict the targets of 26 chemical components and establish the compound-target-disease network. The results were well verified by literatures. The models established in this paper are highly accurate,and can be used to discover potential targets in other traditional Chinese medicine ingredients.

    [Key words] network pharmacology;target prediction;Chuanxiong Rhizoma;cardio-cerebral vascular diseases

    doi:10.4268/cjcmm20141237, 百拇医药(苑婕 李晓杰 陈超 宋向岗 王淑美)
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