Table 3.

Performance (CV-10 fold) of SVM-based models on both balanced and realistic datasets using the composition-motif hybrid features.

Dataset	Feature	Kernel	Thr	Sen	Spec	Acc	MCC	AUC	Parameter
Balanced dataset	AAC_Motif hybrid	t0	0.3	91.55	94.37	92.96	0.86	0.95	t:0,c:60
		t1	0.2	90.14	97.18	93.66	0.88	0.95	t:1,d:1
		t2	0.9	90.14	97.18	93.66	0.88	0.97	g:0.001 c:0.05 j:4
	DPC_Motif hybrid	t0	0.4	87.32	95.07	91.2	0.83	0.97	t:0,c:990
		t1	−0.4	93.66	97.18	95.42	0.91	0.96	t:1,d:2
		t2	0.1	92.25	95.77	94.01	0.88	0.97	g:0.001 c:1 j:1
Realistic dataset	AAC_Motif hybrid	t0	0.3	75.35	99.3	97.13	0.82	0.96	t:0,c:5
		t1	−0.6	89.44	98.38	97.57	0.86	0.98	t:1,d:3
		t2	−0.3	88.73	98.67	97.77	0.87	0.98	g:0.001 c:4 j:1
	DPC_Motif hybrid	t0	0.5	78.87	97.89	96.17	0.77	0.95	t:0,c:990
		t1	−0.3	81.69	99.02	97.45	0.84	0.97	t:1,d:2
		t2	−0.3	85.92	99.02	97.83	0.87	0.97	g:0.001 c:2 j:1

The models constructed using balanced dataset displayed the highest MCC values of 0.88 and 0.91 for AAC_Motif hybrid and DPC_Motif hybrid models, respectively. The models constructed using realistic dataset displayed the highest MCC values of 0.87 for both AAC_Motif hybrid and DPC_Motif Hybrid models.