Table 2.

Evaluation and performance comparison of different models. The average values and standard errors of 6 indicators are calculated in 10 independent runs. The ensemble learning method can achieve better performance compared to single XGBoost and GCNN models.

	Accuracy	Recall	Precision	Specificity	F1 score	ROC AUC
XGBoost	0.969 ± 0.002a	0.799 ± 0.023	0.732 ± 0.030	0.982 ± 0.003	0.764 ± 0.016	0.897 ± 0.016
GCNN	0.923 ± 0.006	0.604 ± 0.023	0.427 ± 0.046	0.943 ± 0.007	0.500 ± 0.031	0.832 ± 0.015
Model ensembling	0.974 ± 0.010	0.847 ± 0.095	0.726 ± 0.085	0.980 ± 0.013	0.782 ± 0.072	0.914 ± 0.018
Allosite11	0.962	0.852	0.688	0.970	0.761	0.911

^aStandard error (SE) = Standard deviation (SD) / $\sqrt{\text{sample size}}$.