基于多特征融合的药物疾病关联预测模型构建

We constructed a drug-disease association prediction model on the basis of drug multi feature fusion, which can provide theoretical foundation for drug knowledge discovery. Three similarities fused into drug comprehensive similarity by drug chemical structure, drug-side effect and drug-target multi-features. Disease similarity was calculated based on MeSH tree number. Next, GCN method was used to extract feature information of drug-disease graph data. Finally, MFFGCN was constructed for drug-disease association prediction. The association of drug diseases was predicted on the same data set, with the help of multiple evaluation indicators such as AUC, AUPR, accuracy, sensitivity, recall and Fl, MFFGCN has better performance than the single feature association prediction method and 4 existing representative algorithms. The AUC index is 0. 8662, which is 2. 48% higher than the average predicted AUC index of single feature and 1. 67% higher than the baseline method. The AUPR index is 0. 3412, which is 1. 67% higher than the average predicted AUC index of single feature and 27. 49% higher than the baseline method. MFFGCN has achieved good performance in the prediction of unknown drug disease association. This methods can find new indications of drugs, and also provide methodological reference and theoretical basis for drug relocation.