Skip to main content

View full-text article in PMC

. 2021 Apr 15;13:29. doi: 10.1186/s13321-021-00508-0

Table 7.

Overall performance of the final linear models for CS4

Classification^b	QSAR-Co^a			QSAR-Co-X
	GA-LDA			FS-LDA			SFS-LDA			SFS-LDA
	GA-LDA			FS-LDA			(Scoring: Accuracy)			(Scoring: AUROC)
	Str^c	Ts^d	Vd^e	Str^c	Ts^d	Vd^e	Str^c	Ts^d	Vd^e	Str^c	Ts^d	Vd^e
TP	941	418	315	940	422	323	934	413	322	930	407	328
TN	932	389	311	925	388	302	947	393	309	956	406	317
FP	67	33	16	74	34	25	52	29	18	43	16	10
FN	97	33	40	98	29	32	104	38	33	108	44	27
Sn (%)	90.65	92.68	88.73	92.59	91.94	92.35	94.79	93.13	94.49	95.7	96.21	96.94
Sp (%)	93.29	92.18	95.11	90.56	93.57	90.99	89.98	91.57	90.7	89.59	90.24	92.39
Acc (%)	91.95	92.44	91.79	91.56	92.78	91.64	92.34	92.32	92.52	92.59	93.13	94.57
MCC^f	0.839	0.849	0.838	0.831	0.855	0.833	0.848	0.847	0.851	0.853	0.864	0.893

The most significant results are highlighted in bold

^aModel previously reported in [21]

^bTP: True positive, TN: True negative, FP: False positive, FN: False negative, Sn: Sensitivity, Sp: Specificity, Acc: Accuracy

^cSub-training set

^dTest set

^eValidation set

^fMatthews correlation coefficient