SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

doi:10.1186/1471-2105-9-226

Table 2 Experimental comparison between SCPRED and competing structural class prediction methods.

From: SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

Test type	Algorithm	Feature vector (# features)	Reference	Accuracy					MCC				GC²
				all-α	all-β	α/β	α+β	overall	all-α	all-β	α/β	α+β
Jackknife	SVM (Gaussian kernel)	CV (20)	[36]	68.6	59.6	59.8	28.6	53.9	0.52	0.42	0.43	0.15	0.17
	LogicBoost with decision tree	CV (20)	[23]	56.9	51.5	45.4	30.2	46.0	0.41	0.32	0.32	0.06	0.10
	Bagging with random tree	CV (20)	[34]	58.7	47.0	35.5	24.7	41.8	0.33	0.26	0.22	0.06	0.06
	LogitBoost with decision stump	CV (20)		62.8	52.6	50.0	32.4	49.4	0.49	0.35	0.34	0.11	0.13
	SVM (3^rd order polyn. kernel)	CV (20)		61.2	53.5	57.2	27.7	49.5	0.46	0.35	0.39	0.11	0.13
	Multinomial logistic regression	custom dipeptides (16)	[28]	56.2	44.5	41.3	18.8	40.2	0.23	0.20	0.31	0.06	0.05
	Information discrepancy¹	dipeptides (400)	[22, 24]	59.6	54.2	47.1	23.5	47.0	0.46	0.40	0.24	0.04	0.12
	Information discrepancy¹	tripeptides (8000)		45.8	48.5	51.7	32.5	44.7	0.39	0.39	0.25	0.06	0.11
	Multinomial logistic regression	custom (34)	[27]	71.1	65.3	66.5	37.3	60.0	0.61	0.51	0.51	0.22	0.25
	SVM with RBF kernel	custom (34)		69.7	62.1	67.1	39.3	59.5	0.60	0.50	0.53	0.21	0.25
	StackingC ensemble	custom (34)		74.6	67.9	70.2	32.4	61.3	0.62	0.53	0.55	0.22	0.26
	Multinomial logistic regression	custom (66)	[26]	69.1	61.6	60.1	38.3	57.1	0.56	0.44	0.48	0.21	0.21
	SVM (1^st order polyn. kernel)	autocorrelation (30)		50.1	49.4	28.8	29.5	34.2	0.16	0.16	0.05	0.05	0.02
	SVM (1^st order polyn. kernel)	custom (58)	[29]	77.4	66.4	61.3	45.4	62.7	0.65	0.54	0.55	0.27	0.28
	Linear logistic regression	custom (58)		75.2	67.5	62.1	44.0	62.2	0.63	0.54	0.54	0.27	0.27
	SVM (Gaussian kernel)	PSI-PRED based (13)	this paper	92.6	79.8	74.9	69.0	79.3	0.87	0.79	0.68	0.55	0.55
	SVM (Gaussian kernel)	custom (8 PSI-PRED based)	this paper	92.6	80.6	73.4	68.5	79.1	0.87	0.79	0.67	0.54	0.54
	SCPRED	custom (9)	this paper	92.6	80.1	74.0	71.0	79.7	0.87	0.79	0.69	0.57	0.55
10-fold cross validation	SVM (Gaussian kernel)	CV (20)	[36]	67.9	59.1	58.1	27.7	53.0	0.51	0.42	0.41	0.14	0.16
	LogicBoost with decision tree	CV (20)	[23]	51.9	53.7	46.5	32.4	46.1	0.38	0.37	0.31	0.07	0.10
	Bagging with random tree	CV (20)	[34]	53.5	51.0	37.6	22.0	41.2	0.28	0.30	0.22	0.04	0.06
	LogitBoost with decision stump	CV (20)		63.2	53.5	50.9	32.4	50.0	0.48	0.36	0.36	0.12	0.14
	SVM (3^rd order polyn. kernel)	CV (20)		61.4	54.0	55.2	27.4	49.2	0.46	0.35	0.37	0.10	0.13
	Multinomial logistic regression	custom dipeptides (16)	[28]	56.9	44.2	42.2	17.7	40.2	0.24	0.20	0.32	0.04	0.06
	Multinomial logistic regression	custom (34)	[27]	69.9	65.3	66.5	38.4	60.0	0.60	0.52	0.51	0.23	0.25
	SVM with RBF kernel	custom (34)		70.2	61.6	67.6	39.6	59.8	0.60	0.49	0.53	0.22	0.25
	StackingC ensemble	custom (34)		73.4	67.3	69.1	29.8	59.9	0.59	0.52	0.54	0.18	0.25
	Multinomial logistic regression	custom (66)	[26]	69.1	60.5	59.5	38.1	56.7	0.56	0.44	0.48	0.20	0.21
	SVM (1^st order polyn. kernel)	autocorrelation (30)		52.4	49.7	0.3	30.4	35.1	0.18	0.16	0.05	0.06	0.02
	SVM (1^st order polyn. kernel)	custom (58)	[29]	77.7	66.8	60.7	45.4	62.8	0.64	0.54	0.54	0.28	0.28
	Linear logistic regression	custom (58)		74.7	66.4	62.7	45.8	62.4	0.63	0.54	0.54	0.27	0.28
	SVM (Gaussian kernel)	PSI-PRED based (13)	this paper	93.2	79.5	75.7	69.4	79.7	0.87	0.79	0.70	0.55	0.55
	SVM (Gaussian kernel)	custom (8 PSI-PRED based)	this paper	92.5	80.4	73.7	68.0	79.0	0.87	0.79	0.67	0.54	0.54
	SCPRED	custom (9)	this paper	92.8	80.6	74.3	71.4	80.1	0.87	0.79	0.70	0.57	0.56

¹This method was not originally tested using 10-fold cross validation and thus we also did not report these results

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