Table 4.

The performance comparison between different methods on the Yeast dataset.

Author	Model	Accu.(%)	Sen.(%)	Prec.(%)	MCC(%)
Yangs’ work40	Cod1	75.08 ± 1.13	75.81 ± 1.20	74.75 ± 1.23	N/A
	Cod2	80.04 ± 1.06	76.77 ± 0.69	82.17 ± 1.35	N/A
	Cod3	80.41 ± 0.47	78.14 ± 0.90	81.86 ± 0.99	N/A
	Cod4	86.15 ± 1.17	81.03 ± 1.74	90.24 ± 0.45	N/A
Zhous’ work41	SVM + LD	88.56 ± 0.33	87.37 ± 0.22	89.50 ± 0.60	77.15 ± 0.68
Yous’ work42	PCA-EELM	87.00 ± 0.29	86.15 ± 0.43	87.59 ± 0.32	77.36 ± 0.44
Guos’ work30	ACC	89.33 ± 2.67	89.93 ± 3.68	88.87 ± 6.16	N/A
	AC	87.36 ± 1.38	87.30 ± 4.68	87.82 ± 4.33	N/A
Wangs’ work43	SAE	96.60 ± 0.22	93.73 ± 0.46	99.36 ± 0.41	93.41 ± 0.41
Dus’ work44	DeepPPI	94.43 ± 0.30	N/A	96.65 ± 0.59	88.97 ± 0.62
Zhangs’ work45	EnsDNN	95.29 ± 0.43	95.12 ± 0.45	95.45 ± 0.89	90.59 ± 0.86
Patels’ work46	DeepInteract	92.67	86.85	98.31	85.96
Our model	CNN-FSRF	97.75 ± 0.54	99.61 ± 0.22	95.89 ± 1.02	96.04 ± 1.05