Table 3.

Prediction performance of different classifiers for vitamin B-interacting residues (VBIRs)

Feature	Classifier	SN	SP	ACC	MCC
Binary	SVM (Threshold = −0.8)	73.22 ± 0.36	67.00 ± 0.49	67.57 ± 0.47	0.24 ± 0.00
	SVM (Threshold = −0.6)	30.36 ± 0.62	96.69 ± 0.12	90.66 ± 0.11	0.33 ± 0.01
	BayesNet	63.25 ± 0.56	66.23 ± 0.73	65.96 ± 0.62	0.18 ± 0.00
	ComplementNaiveBayes	68.69 ± 0.52	68.51 ± 0.23	68.52 ± 0.18	0.23 ± 0.00
	NaiveBayes	37.74 ± 0.90	90.45 ± 0.23	85.66 ± 0.14	0.25 ± 0.01
	NaiveBayesMultinomial	44.22 ± 0.43	87.54 ± 0.24	83.60 ± 0.19	0.25 ± 0.00
	IBk	30.81 ± 0.71	93.33 ± 0.17	87.65 ± 0.14	0.24 ± 0.01
	RandomForest	39.33 ± 1.08	79.36 ± 0.37	75.72 ± 0.36	0.13 ± 0.01
PSSM	SVM (Threshold = −0.8)	83.33 ± 0.36	80.51 ± 0.13	80.77 ± 0.14	0.42 ± 0.00
	SVM (Threshold =0.1)	55.57 ± 0.63	98.04 ± 0.10	94.18 ± 0.09	0.61 ± 0.01
	BayesNet	71.65 ± 1.13	66.14 ± 0.08	66.64 ± 0.10	0.23 ± 0.01
	ComplementNaiveBayes	63.90 ± 1.26	81.73 ± 0.28	80.11 ± 0.22	0.32 ± 0.01
	NaiveBayes	72.28 ± 1.22	66.44 ± 0.09	66.97 ± 0.12	0.23 ± 0.01
	NaiveBayesMultinomial	21.22 ± 0.69	98.88 ± 0.03	91.82 ± 0.06	0.34 ± 0.01
	IBk	56.74 ± 0.80	98.04 ± 0.07	94.28 ± 0.11	0.62 ± 0.01
	RandomForest	39.16 ± 0.56	97.74 ± 0.09	92.41 ± 0.10	0.46 ± 0.01

*Bold value indicates highest SVM performance with balanced sensitivity and specificity.

**Italic value indicates SVM/IBk performance with highest MCC.

The values of standard errors are also given with performances.