Table 4 Accuracy comparison of PROTNN with other classification techniques

From: ProtNN: fast and accurate protein 3D-structure classification in structural and topological space

Dataset	Classification approach
	Blast	Sheba	FatCat	CE	LPGBCMP	D&D	GAIA	ProtNN	ProtNN*
DS1	0.88	0.81	1	0.45	0.88	0.93	1	0.97	0.97
DS2	0.82	0.86	0.89	0.49	0.73	0.76	0.66	0.8	0.89
DS3	0.9	0.95	0.84	0.59	0.90	0.96	0.89	0.96	0.97
DS4	0.76	0.92	1	0.46	0.9	0.93	0.89	0.97	0.97
DS5	0.86	0.99	0.94	0.76	0.87	0.89	0.72	0.9	0.94
DS6	0.78	1	0.94	0.81	0.91	0.95	0.87	0.96	0.96
Avg. accuracy¹	0.83 ±0.05	0.92 ±0.07	0.94 ±0.06	0.59 ±0.15	0.86 ±0.06	0.9 ±0.07	0.84 ±0.12	0.93 ±0.06	0.95 ±0.03
Avg. distances²	0.14 ±0.07	0.05 ±0.07	0.04 ±0.05	0.38 ±0.15	0.11 ±0.03	0.7 ±0.04	0.14 ±0.09	0.05 ±0.03	0.02 ±0.01
Rank	8	4	2	9	6	5	7	3	1

¹Average classification accuracy of each classification approach over the six datasets
²Average of the distances between the accuracy of each approach and the best obtained accuracy with each dataset
The boldface numbers highlight the best performance

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