Table 4.

Average AUC and Partial AUC scores in Human.

Method	AUC mean	AUC std	R50 mean	R50 std	R100 mean	R100 std
LR	0.9419	0.020	0.1148	0.031	0.1684	0.031
NB	0.9389	0.003	0.0964	0.031	0.1356	0.035
RF	0.9427	0.009	0.0740	0.025	0.1263	0.030
SVM	0.7645	0.091	0.0455	0.028	0.0589	0.040
MFE	0.9608	0.007	0.1341	0.023	0.1759	0.027
MFE-FM	0.9384	0.018	0.1297	0.023	0.1713	0.025

Average AUC and partial AUC scores for six classification methods for PPI prediction in human. LR: Logistic regression; NB: Naive Bayes; RF: Random Forest; SVM: Support Vector Machine; MFE: Mixture-of-Feature-Experts; MFE-FM: Mixture-of-Feature-Experts with missing values filled. Average AUC and partial AUC scores are reported and the standard derivations for each score estimation are also listed in the table. MFE scores are highlighted and it again achieves better AUC/R50/R100 scores compared to the other five classifiers.