Table 5. Combined performance statistics of SVM classifiers employing solo features and hybrid approaches in predicting O-glycosites (using balanced dataset).

Feature	Sensitivity (%)	Specificity (%)	Accuracy (%)	MCC (%)	AUC (%)
CPP	68.10	72.41	70.26	0.41	0.74071
CPP+SS	70.69	71.55	71.12	0.42	0.75743
CPP+ASA	67.24	75.00	71.12	0.42	0.75780
CPP+SS+ASA	72.41	75.00	73.71	0.47	0.76955
BPP	66.38	67.24	66.81	0.34	0.73023
BPP+SS	69.83	68.10	68.97	0.38	0.74160
BPP+ASA	77.59	61.21	69.40	0.39	0.71143
BPP+SS+ASA	65.52	72.41	68.97	0.38	0.73766
PPP	75.00	71.55	73.28	0.47	0.81250
PPP+SS	73.28	73.28	73.28	0.47	0.76806
PPP+ASA	74.14	71.55	72.84	0.46	0.77341
PPP+SS+ASA	77.59	69.83	73.71	0.48	0.76925

Footnotes: BPP- Binary profile of patterns, CPP- Composition profile of patterns, PPP- PSSM profile of patterns, MCC- Matthews correlation coefficient, AUC- Area under curve, SS-secondary structure and ASA- Accessible surface area.