A mixture of feature experts approach for protein-protein interaction prediction

BMC Bioinformatics

Table 3 Average AUC and Partial AUC scores in Yeast.

Method	AUC mean	AUC std	R50 mean	R50 std	R100 mean	R100 std
LR	0.8823	0.033	0.2866	0.070	0.3546	0.073
NB	0.9349	0.015	0.2486	0.047	0.3135	0.062
RF	0.9321	0.014	0.2688	0.048	0.3434	0.049
SVM	0.9159	0.024	0.2585	0.063	0.3262	0.067
MFE	0.9463	0.013	0.3080	0.078	0.3799	0.077
MFE-FM	0.9220	0.021	0.2918	0.061	0.3738	0.058

Average AUC and partial AUC scores for six classification methods for PPI prediction in yeast. 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 features 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 clearly achieves better AUC/R50/R100 scores compared to the other five.

ISSN: 1471-2105