CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model

BMC Bioinformatics

Table 4 Performance of comparison models and CL-ACP on the ACP datasets

Dataset	Methods	Acc (%)	Sens (%)	Spec (%)	Prec (%)	Mcc (%)	AUC
ACP736	SVM	80.97	81.86	80.06	81.06	61.97	0.810
	RF	81.52	81.06	82.00	82.44	63.08	0.815
	NB	75.41	90.13	60.14	70.34	52.87	0.751
	PTPD	80.97	81.86	80.06	81.06	61.97	0.884
	ACP-DL	80.81	81.39	80.22	81.00	61.67	0.890
	AntiCP2.0	81.21	87.59	74.85	79.13	62.87	0.843
	iACP-DRLF	80.72	86.68	74.24	78.74	61.38	0.859
	CL-ACP	83.83	82.93	84.76	85.15	67.86	0.909
ACP240	SVM	79.58	83.01	75.61	80.22	59.59	0.793
	RF	81.66	84.58	78.30	82.05	63.48	0.814
	NB	70.83	88.40	50.43	67.35	43.01	0.694
	PTPD	79.58	83.01	75.61	80.22	59.59	0.784
	ACP-DL	83.75	88.40	78.45	83.16	68.29	0.903
	AntiCP2.0	84.00	88.64	76.16	84.18	71.19	0.894
	iACP-DRLF	84.11	88.01	74.35	84.03	70.35	0.903
	CL-ACP	87.92	90.74	84.76	88.41	76.56	0.935
ACP539	SVM	76.80	38.71	97.70	89.88	48.34	0.682
	RF	76.80	45.46	93.96	79.93	46.88	0.698
	NB	75.41	55.13	90.14	78.34	50.87	0.606
	PTPD	74.94	37.09	95.70	82.65	42.82	0.740
	ACP-DL	72.72	60.08	80.34	65.43	41.37	0.831
	AntiCP2.0	82.38	69.25	95.00	85.27	60.09	0.881
	iACP-DRLF	82.56	65.21	92.00	82.16	60.99	0.882
	CL-ACP	84.41	77.48	88.23	78.46	65.98	0.921

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