Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM

Table 3

Different methods on Yeast dataset performance comparison.

Model	Test set	Accu. (%)	Prec. (%)	Sen. (%)	MCC (%)
Guo et al.’s  work [9]	ACC	89.33 ± 2.67	88.87 ± 6.16	89.93 ± 3.68	N/A
	AC	87.36 ± 1.38	87.82 ± 4.33	87.30 ± 4.68	N/A
You et al.’s work [25]	PCA-EELM	87.00 ± 0.29	87.59 ± 0.32	86.15 ± 0.43	77.36 ± 0.44
Yang et al.’s work [56]	Cod1	75.08 ± 1.13	74.75 ± 1.23	75.81 ± 1.20	N/A
	Cod2	80.04 ± 1.06	82.17 ± 1.35	76.77 ± 0.69	N/A
	Cod3	80.41 ± 0.47	81.86 ± 0.99	78.14 ± 0.90	N/A
	Cod4	86.15 ± 1.17	90.24 ± 0.45	81.03 ± 1.74	N/A
Zhou et al.’s work [57]	SVM + LD	88.56 ± 0.33	89.50 ± 0.60	87.37 ± 0.22	77.15 ± 0.68
Our method	SVM + PSSM	95.86 ± 0.34	96.46 ± 0.50	95.21 ± 0.70	92.06 ± 0.62
	RF + PSSM	97.77 ± 0.29	99.96 ± 0.08	95.57 ± 0.70	95.64 ± 0.55