Table 2. Assessment of performance using the original datasets through fivefold cross-validation.

	Sample ratio = 1:1 (mean±S.D)				Sample ratio = 1:10 (mean±S.D)
	Activatory DTIs	Inhibitory DTIs	Activatory DTIs	Inhibitory DTIs	Activatory DTIs	Inhibitory DTIs	Activatory DTIs	Inhibitory DTIs
	AUROC	AUPR	AUROC	AUPR	AUROC	AUPR	AUROC	AUPR
Logit	0.725±0.032	0.690±0.041	0.823±0.007	0.806±0.006	0.697±0.027	0.166±0.016	0.823±0.007	0.340±0.004
RF	0.868±0.022	0.890±0.021	0.921±0.004	0.932±0.003	0.858±0.026	0.559±0.054	0.923±0.004	0.729±0.010
MLP	0.841±0.029	0.851±0.035	0.920±0.004	0.918±0.006	0.835±0.020	0.379±0.038	0.916±0.004	0.559±0.018
CDF#	0.876±0.021	0.899±0.020	0.934±0.004	0.945±0.004	0.871±0.021	0.611±0.046	0.936±0.004	0.775±0.011

Boldface indicates the highest value for each performance metric. Logit, logistic regression; RF, random forest; MLP, multilayer perceptron; CDF, cascade deep forest.

^# CDF model with 2 estimators in each cascade layer and 100 trees in each forest.