title: [Study on prediction of compound-target-disease network of chuanxiong rhizoma based on random forest algorithm].
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.
authors.forename: Jie
authors.initials: J
authors.lastname: Yuan
authors.forename: Xiao-Jie
authors.initials: XJ
authors.lastname: Li
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
authors.forename: Xiang-Gang
authors.initials: XG
authors.lastname: Song
authors.forename: Shu-Mei
authors.initials: SM
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004365:Drugs, Chinese Herbal / Q000494:pharmacology*; D053263:Gene Regulatory Networks / Q000187:drug effects*; D006801:Humans; D030004:Ligusticum / Q000737:chemistry*; D058990:Molecular Targeted Therapy; D027343:Rhizome / Q000737:chemistry*
chemical_list: D004365:Drugs, Chinese Herbal