Table 2.

Performance comparison of different methods using five-fold cross-validation for different datasets.

Dataset	Method	AUC	ACC	SEN	SPE	MCC	PPV
Sollner	BEEPro	0.9987	0.9929	0.9604	0.9946	0.9281	0.9042
AntiJen#1	BEEPro	0.9930	0.9731	0.9680	0.9735	0.8491	0.7688
AntiJen#2	BEEPro	0.9907	0.9580	0.9700	0.9562	0.8402	0.7668
	LEPS	NA	0.7381	0.2672	0.8448	0.1010	0.2885
	BepiPred	NA	0.5552	0.5179	0.5761	0.0604	0.2202
	ABCPred0.8	NA	0.4470	0.6733	0.4040	0.0546	0.2183
	BCPred	NA	0.5392	0.5884	0.5487	0.0893	0.2334
	FBCPred	NA	0.5145	0.6031	0.5121	0.0673	0.2233
HIV	BEEPro	0.9907	0.9454	0.9490	0.9433	0.8853	0.9098
	LEPS	NA	0.6345	0.4833	0.7484	0.2276	0.7144
	BepiPred	0.6000	0.5672	0.5016	0.6085	0.0972	0.6122
	ABCPred0.7	NA	0.5659	0.8797	0.1465	0.0564	0.5633
	BCPred	NA	0.6657	0.8018	0.5457	0.2980	0.6555
	FBCPred	NA	0.6713	0.7320	0.5820	0.2781	0.6556
Pellequer	BEEPro	0.9874	0.9373	0.9256	0.9435	0.8621	0.8935
	BepiPred	0.6710	NA	NA	NA	NA	NA
PC	BEEPro	0.9950	0.9550	0.9708	0.9468	0.9036	0.9058
	LEPS	NA	0.6166	0.1278	0.8833	0.0365	0.4512
	BepiPred	NA	0.5533	0.4823	0.5972	0.0749	0.3819
	ABCPred0.8	NA	0.4889	0.6546	0.4026	0.0513	0.3621
	BCPred	NA	0.5283	0.5092	0.5935	0.0443	0.3607
	FBCPred	NA	0.5220	0.5103	0.5255	0.0317	0.3526
Benchmark	BEEPro	0.9100	0.9200	0.7100	0.9400	0.5700	0.5200
	CBTOPE	0.8900	0.8400	0.8000	0.8500	NA	0.3100
	DiscoTope	0.6000	0.7500	0.4200	0.7900	NA	0.1600
	CEP	0.5400	0.7400	0.3100	0.7800	NA	0.1100
	ClusPro(DOT) best model	0.6900	0.8900	0.4500	0.9300	NA	0.3900
	Patch Dock best model	0.6600	0.8500	0.4300	0.8900	NA	0.2600
	PSI-PRED best patch	0.6000	0.8200	0.3300	0.8600	NA	0.1900
	ProMate	0.5100	0.8400	0.0900	0.9200	NA	0.1000

¹LEPS, BepiPred, ABCPred, BCPred, FBCPred performances were previously compiled by Wang et al. [9]

²CBTOPE, DiscoTope, CEP, ClusPro, Patch Dock, PSI-PRED, ProMate performances were previously compiled by Ansari and Raghava [13]