Table 8.

Results of the experimental comparison between the proposed MODAS method and competing structural class prediction methods on the D1189 dataset.

Classifier used (name of the method, if any)	Feature vector	Reference	Accuracy
			α	β	α/β	α+β	overall
SVM	AA composition, autocorrelations, and physicochemical properties	73	-	-	-	-	52.1
Bayesian classifier	AA composition	81	54.8	57.1	75.2	22.2	53.8
Logistic regression	AA composition, autocorrelations, and physicochemical properties	73	60.2	60.5	55.2	33.2	53.9
SVM	AA and polypeptide composition, physicochemical properties	45	-	-	-	-	54.7
Nearest neighbor	Pseudo-amino acid composition	67	48.9	59.5	81.7	26.6	56.9
Ensemble	AA composition, autocorrelations, and physicochemical properties	72	-	-	-	-	58.9
Nearest neighbor	Composition of tripeptides	52	-	-	-	-	59.9
IB1	PSI Blast based collocated AA pairs	75	65.3	67.7	79.9	40.7	64.7
Discriminant analysis	custom	78	62.3	67.7	63.1	66.5	65.2
SVM with RBF kernel (SCEC)	PSI Blast based collocated AA pairs	75	75.8	75.2	82.6	31.8	67.6
SVM with RBF kernel (SCPRED)	custom	79	89.1	86.7	89.6	53.8	80.6
SVM with polynomial or RBF kernels (MODAS)	custom	this paper	92.3	87.1	87.9	65.4	83.5

The results were obtained using jackknife test. The methods are ordered by their average accuracies. Best results are shown in bold and "---" indicates results that were not reported by the original authors and which cannot be duplicated.