Table 3.

The performance of seven VDA prediction methods on three datasets.

Datasets	Methods	Sensitivity	Specificity	F1 score	Accuracy	AUC
Dataset 1	NGRHMDA	0.578 3	0.556 7	0.061 5	0.557 2	0.645 9
	SMiR-NBI	0.833 1	0.193 6	0.038 5	0.207 9	0.572 3
	LRLSHMDA	0.803 4	0.581 3	0.111 9	0.586 3	0.840 3
	VDA-KATZ	0.697 6	0.668 4	0.151 7	0.669 1	0.880 3
	VDA-RWR	0.482 4	0.783 1	0.115 3	0.827 8	0.858 2
	VDA-BiRW	0.832 3	0.636 8	0.133 2	0.641 1	0.876 5
	VDA-RWLRLS	0.562 6	0.838 0	0.225 9	0.831 9	0.885 8
Dataset 2	NGRHMDA	0.454 4	0.356 2	0.021 8	0.358 1	0.301 1
	SMiR-NBI	0.834 9	0.094 2	0.033 6	0.108 1	0.415 6
	LRLSHMDA	0.783 8	0.484 0	0.073 3	0.489 6	0.824 8
	VDA-KATZ	0.551 2	0.757 4	0.080 5	0.753 5	0.829 6
	VDA-RWR	0.502 2	0.664 3	0.057 4	0.661 3	0.667 5
	VDA-BiRW	0.557 4	0.752 4	0.110 5	0.748 7	0.832 2
	VDA-RWLRLS	0.513 3	0.826 4	0.123 2	0.820 5	0.835 5
Dataset 3	NGRHMDA	0.358 2	0.408 1	0.011 9	0.407 4	0.255 4
	SMiR-NBI	0.923 0	0.042 7	0.023 0	0.053 6	0.436 5
	LRLSHMDA	0.812 9	0.523 9	0.055 2	0.527 5	0.816 9
	VDA-KATZ	0.711 6	0.566 6	0.062 6	0.568 4	0.847 8
	VDA-RWR	0.505 3	0.705 7	0.055 6	0.703 2	0.712 3
	VDA-BiRW	0.707 8	0.574 1	0.072 6	0.575 8	0.851 1
	VDA-RWLRLS	0.519 8	0.843 8	0.118 9	0.844 6	0.862 5