Table 2 Open in new tab The performance...

Table 2

The performance of the existing predictors and PreTP-EL on independent test datasets in terms of the AUC

Datasets	PPTPP [12]	PEPred-Suite [13]	Methods^a	PreTP-EL
AAP	0.770	0.804	0.742 (AntiAngioPred)	0.819
ABP	0.988	0.976	0.976 (AntiBP)	0.992
ACP	0.883	0.949	0.939 (ACPred-FL)	0.950^b
AIP	0.720	0.751	0.795 (AIPpred)	0.810
AVP	0.946	0.949	0.931 (AVPpred)	0.951
CPP	0.965	0.952	0.941 (CPPred-RF)	0.978
PBP	0.740	0.804	0.742 (PSBinder)	0.809
QSP	0.944	0.960	0.903 (QSPpred)	0.965
Ave.^c	0.869	0.893	0.871	0.909

Datasets	PPTPP [12]	PEPred-Suite [13]	Methods^a	PreTP-EL
AAP	0.770	0.804	0.742 (AntiAngioPred)	0.819
ABP	0.988	0.976	0.976 (AntiBP)	0.992
ACP	0.883	0.949	0.939 (ACPred-FL)	0.950^b
AIP	0.720	0.751	0.795 (AIPpred)	0.810
AVP	0.946	0.949	0.931 (AVPpred)	0.951
CPP	0.965	0.952	0.941 (CPPred-RF)	0.978
PBP	0.740	0.804	0.742 (PSBinder)	0.809
QSP	0.944	0.960	0.903 (QSPpred)	0.965
Ave.^c	0.869	0.893	0.871	0.909

^aThe results are reported in [13] and the method is following the corresponding results.

^bPreTP-EL utilizes one more feature extraction method Bit20(NTCT = 2) [13] on ACP dataset.

^cAve. represents the average value of each predictor over eight datasets.