Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

BMC Bioinformatics

Table 2 Results on the average of accuracy, precision, recall, and F-measure by our proposed methods and NWE.

	SVM+SVM		SVM+RVM		SVM		NWE
α	0	0.5	0	0.5	0	0.5
accuracy	0.885	0.907	0.810	0.853	0.861	0.876	-
precision	0.936	0.869	0.847	0.899	0.909	0.873	0.352
recall	0.840	0.926	0.770	0.766	0.819	0.862	0.218
F-measure	0.880	0.891	0.767	0.810	0.854	0.862	0.270

'SVM+SVM' and 'SVM+RVM' denote two-phase methods using SVM and RVM as the second classifier, respectively. 'SVM' denotes usual SVM using only features f⁽¹⁾. α denotes the coefficient of the domain composition kernel K_c. Note that the accuracy is not defined for NWE because it is unsupervised, and predict protein complexes of various sizes. The precision and recall for NWE were calculated as TP divided by the numbers of predicted and known heterotrimers, respectively.

ISSN: 1471-2105