Table 7. Open in new tab Result of the... | Oxford Academic

Table 7.

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Result of the 10-fold CV on the NetOGlyc-4.0 extracellular training dataset using various models when training datasets are encoded with Ankh PLM. The highest value in each column have been highlighted in bold.

Models	MCC ± 1 SD	ACC ± 1 SD	SN ± 1 SD	SP ± 1 SD
LR	0.579 ± 0.052	0.789 ± 0.026	0.798 ± 0.032	0.780 ± 0.033
XGBoost	0.582 ± 0.091	0.790 ± 0.045	0.819 ± 0.050	0.761 ± 0.049
Random forest	0.561 ± 0.069	0.780 ± 0.034	0.795 ± 0.049	0.764 ± 0.044
SVM	0.613 ± 0.080	0.805 ± 0.040	0.824 ± 0.051	0.786 ± 0.047
MLP	0.632 ± 0.056	0.814 ± 0.028	0.828 ± 0.067	0.800 ± 0.039

Models	MCC ± 1 SD	ACC ± 1 SD	SN ± 1 SD	SP ± 1 SD
LR	0.579 ± 0.052	0.789 ± 0.026	0.798 ± 0.032	0.780 ± 0.033
XGBoost	0.582 ± 0.091	0.790 ± 0.045	0.819 ± 0.050	0.761 ± 0.049
Random forest	0.561 ± 0.069	0.780 ± 0.034	0.795 ± 0.049	0.764 ± 0.044
SVM	0.613 ± 0.080	0.805 ± 0.040	0.824 ± 0.051	0.786 ± 0.047
MLP	0.632 ± 0.056	0.814 ± 0.028	0.828 ± 0.067	0.800 ± 0.039

Table 7.

Open in new tab

Result of the 10-fold CV on the NetOGlyc-4.0 extracellular training dataset using various models when training datasets are encoded with Ankh PLM. The highest value in each column have been highlighted in bold.

Models	MCC ± 1 SD	ACC ± 1 SD	SN ± 1 SD	SP ± 1 SD
LR	0.579 ± 0.052	0.789 ± 0.026	0.798 ± 0.032	0.780 ± 0.033
XGBoost	0.582 ± 0.091	0.790 ± 0.045	0.819 ± 0.050	0.761 ± 0.049
Random forest	0.561 ± 0.069	0.780 ± 0.034	0.795 ± 0.049	0.764 ± 0.044
SVM	0.613 ± 0.080	0.805 ± 0.040	0.824 ± 0.051	0.786 ± 0.047
MLP	0.632 ± 0.056	0.814 ± 0.028	0.828 ± 0.067	0.800 ± 0.039

Models	MCC ± 1 SD	ACC ± 1 SD	SN ± 1 SD	SP ± 1 SD
LR	0.579 ± 0.052	0.789 ± 0.026	0.798 ± 0.032	0.780 ± 0.033
XGBoost	0.582 ± 0.091	0.790 ± 0.045	0.819 ± 0.050	0.761 ± 0.049
Random forest	0.561 ± 0.069	0.780 ± 0.034	0.795 ± 0.049	0.764 ± 0.044
SVM	0.613 ± 0.080	0.805 ± 0.040	0.824 ± 0.051	0.786 ± 0.047
MLP	0.632 ± 0.056	0.814 ± 0.028	0.828 ± 0.067	0.800 ± 0.039