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Table 3 The average test performance of conventional ML models on the 12 prediction tasks in 5 repeated experiments

From: Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

Tasks KNN NN RF SVM XGBoost
AUC Std. AUC Std. AUC Std. AUC Std. AUC Std.
NR-AR-LBD 0.6955 0.6671 0.0244 0.7323 0.0267 0.6795 0.6784
NR-AR 0.6527 0.6806 0.0088 0.6836 0.0266 0.7193 0.6818
NR-AhR 0.7639 0.7628 0.0177 0.8243 0.0074 0.7794 0.8287
NR-Aromatase 0.5576 0.5127 0.0772 0.6900 0.0092 0.6873 0.7106
NR-ER-LBD 0.6191 0.5387 0.1171 0.6169 0.0300 0.6078 0.6250
NR-ER 0.6597 0.6549 0.0162 0.6316 0.0080 0.6126 0.6745
NR-PPAR-gamma 0.6182 0.5558 0.0736 0.7135 0.0258 0.6454 0.6414
SR-ARE 0.6366 0.5656 0.0251 0.6603 0.0018 0.6843 0.6640
SR-ATAD5 0.5866 0.6240 0.0537 0.6928 0.0189 0.6546 0.6841
SR-HSE 0.6574 0.6143 0.0222 0.6852 0.0131 0.6858 0.6647
SR-MMP 0.7057 0.6551 0.0612 0.7818 0.0065 0.7794 0.7656
SR-p53 0.6778 0.5963 0.0075 0.7263 0.0130 0.7051 0.6942
  1. The bold number denotes the best result among all conventional ML models in the corresponding task