Table 2.

Comparison of the predictive performance of our BEC-Pred model with other machine learning methods at level 3 of the EC number

Data	ACC	MCC	F1 score
KNN-morgan2	$0.843\pm 0.005$	$0.827\pm 0.005$	$0.843\pm 0.005$
RF-morgan2	$0.768\pm 0.003$	$0.744\pm 0.005$	$0.739\pm 0.005$
DNN—morgan2	$0.861\pm 0.005$	$0.847\pm 0.005$	$0.848\pm 0.006$
KNN-drfp	$0.837\pm 0.006$	$0.820\pm 0.005$	$0.837\pm 0.006$
DNN-drfp	$0.852\pm 0.004$	$0.834\pm 0.005$	$0.837\pm 0.004$
GNN	$0.861\pm 0.002$	$0.847\pm 0.002$	$0.854\pm 0.003$
BEC-Pred (ours)	$0.916\pm 0.003$	$0.907\pm 0.003$	$0.913\pm 0.004$

The evaluation criteria used include accuracy, MCC, and F1 scores