Hypertension-Related Drug Activity Identification Based on Novel Ensemble Method - Yang, Bin; Bao, Wenzheng; Wang, Jinglong (Frontiers in Genetics, 2021)
Hypertension is a chronic disease, which is a major risk factor for factor of cardiovascular and cerebrovascular diseases and often leads to damage to target organs. The prevention and treatment of hypertension has become crucial important for human health. In this paper, a novel ensemble method based on flexible neural tree (FNT) is proposed to identify hypertension-related active compounds. In the ensemble method, the base classifiers are Multi-Grained Cascade Forest (gcForest), support vector machines (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logical regression and naïve Bayes (NB). The classification results of nine classifiers are utilized as the input vector of FNT, which is utilized as nonlinear ensemble method to disease-related drug activity. The experiment data are extracted from hypertension-nonrelated and hypertension-related compounds collected form the up-to-date literatures. The results reveal that our proposed ensemble method performs better than other single classifiers in terms of ROC curve, AUC, TPR, FRP, Precision, Specificity and F1. Our method is also compared with the averaged and voting ensemble methods. The results reveal that our method could identify disease-related compounds more accurately than two classical ensemble methods.