Table 7.

Performance scores and validation scheme of the methods involved in this review

Study	Algorithms	Validation scheme	AUROC	AUPRC	ACC	F1	MCC	Precision	Sensitivity	Specificity
Pratapa et al. [22]	SVM	10-fold cross-validation			0.796
Wu et al. [71]	k-NN	10-fold cross-validation	0.848	0.861	0.764	0.739		0.825	0.670
Pandey et al. [72]	MNMC	10-fold cross-validation	0.897
Wu et al. [73]	Ensemble learning	5-fold cross-validation	0.871
Li et al. [24]	RF	10-fold cross-validation					0.532
Benstead-Hume et al. [25]	RF	5-fold cross-validation	0.889
Liu et al. [29]	Logistic matrix factorization	5-fold cross-validation	0.848	0.239
Huang et al. [28]	Matrix factorization	5-fold cross-validation	0.923
Liany et al. [30]	CMF	3-fold cross-validation	0.980	0.980
Wan et al. [41]	Neural network	5-fold cross-validation	0.969	0.880	0.959	0.866		0.872	0.903	0.968
Cai et al. [31]	GCN	5-fold cross-validation	0.878	0.344		0.552
Long et al. [32]	GAT	5-fold cross-validation	0.937	0.948
Hao et al. [33]	GAE	5-fold cross-validation	0.917	0.942		0.871
Wang et al. [80]	KG	5-fold cross-validation	0.947	0.956		0.887

Notes: AUROC, area under receiver optimizer characteristics curve; AUPRC, area under precision-recall curve; ACC, accuracy; MCC, Matthews correlation coefficient.