IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach

Table 1

Analysis of various classifiers using feature encoding schemes on training and testing datasets D_train and D_test.

Feature-encoding methods	Classifiers	Benchmark dataset					Independent dataset
		ACC (%)	SN (%)	SP (%)	MCC	F-measure (%)	ACC (%)	SN (%)	SP (%)	MCC	F-measure (%)
APAAC	KNN	95.01	94.55	95.26	0.898	92.95	88.93	97.50	83.33	0.777	85.71
	DT	90.70	87.27	92.66	0.802	86.92	91.96	92.50	91.66	0.829	89.15
	SVM	89.72	86.36	91.66	0.786	85.61	81.25	82.50	80.55	0.612	75.86
	HDnet	95.69	91.82	93.75	0.909	93.83	90.17	87.50	91.66	0.787	86.41
DPC	KNN	90.41	91.82	89.63	0.809	86.77	92.85	97.50	90.27	0.854	90.69
	DT	89.39	85.45	91.66	0.771	85.11	93.75	92.05	94.44	0.864	91.35
	SVM	90.23	96.36	45.58	0.467	69.67	83.03	100	73.61	0.706	80.80
	HDnet	96.02	90.00	99.47	0.916	94.18	91.96	77.50	100	0.829	87.32
FEGS	KNN	93.03	90.00	94.78	0.856	89.71	94.64	100	91.60	0.890	93.00
	DT	91.93	87.27	93.74	0.817	87.95	93.75	97.50	91.60	0.871	91.76
	SVM	94.72	94.55	94.85	0.889	92.96	93.75	100	90.27	0.876	91.95
	HDnet	98.00	94.55	100	0.958	96.94	99.10	97.50	100	0.980	98.73