Table 3.

Performance of different classification models developed using support vector machine as machine learning technique

Feature	Thre	Sen	Spec	Acc	MCC	AUC	Parameters
Performance on training data
AAC	0.6	73.58	70.07	72.92	0.36	0.77	t:2 g:0.005 c:80 j:1
DPC	0.4	86.11	62.04	81.53	0.45	0.8	t:2 g:0.001 c:10 j:1
PHY	0.7	91.25	24.82	78.61	0.20	0.57	t:2 g:0.001:c:50:j:4
DPCHyb_NONE	0.4	87.82	62.04	82.92	0.48	0.84	t:2 g:0.001 c:20 j:1
DPCHyb_KOOL	0.4	89.54	60.58	84.03	0.49	0.85	t:2 g:0.001 c:4 j:2
DPCHyb_BETTS	0.3	93.65	62.04	87.64	0.58	0.88	t:2 g:0.001 c:8 j:3
Performance on validation data
DPCHyb_BETTS	0.3	91.1	50	83.33	0.43	0.71

The hybrid model prepared using Dipeptide composition based features and MERCI displayed the best performance with an accuracy of 87.6 %. The same model showed an accuracy of 83.3 % on validation dataset