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Table 4 Choice of learning algorithms. Learning algorithms tested: LR – logistic regression; NN – neural network of 5 hidden nodes; NNE – bagging ensemble of 10 NNs; SVM/linear – linear support vector machine (inner-product kernel), with C = 0.5 for VSL2-S and C = 1 for VSL2-L; SVM/RBF – nonlinear support vector machine (radius-based-function kernel), with C = 2, gamma = 2-4 for VSL2-S and C = 1, gamma = 2-2 for VSL2-L. All 54 features were included to build the predictor models. The SVM parameters were selected by embedded 5-fold cross-validation (see Predictor model).

From: Length-dependent prediction of protein intrinsic disorder

 

Learning algorithm

SN S

SN L

SP

ACC S /ACC L

 

LR

81.8 ± 1.1

70.2 ± 1.9

81.9 ± 0.3

81.8 ± 0.6

VSL2-S

NN

81.1 ± 1.1

68.6 ± 1.9

82.0 ± 0.3

81.5 ± 0.6

 

NNE

81.5 ± 1.1

68.8 ± 2.0

83.3 ± 0.3

82.4 ± 0.6

 

SVM/linear

82.0 ± 1.1

70.7 ± 1.9

81.5 ± 0.3

81.7 ± 0.6

 

SVM/RBF

81.0 ± 1.1

70.0 ± 1.9

81.0 ± 0.3

81.0 ± 0.6

 

LR

42.1 ± 1.8

80.3 ± 2.2

87.8 ± 0.5

84.0 ± 1.2

VSL2-L

NN

31.3 ± 1.7

76.6 ± 2.4

90.3 ± 0.5

83.4 ± 1.2

 

NNE

31.9 ± 1.7

76.1 ± 2.4

91.9 ± 0.5

84.0 ± 1.2

 

SVM/linear

44.1 ± 1.8

82.1 ± 2.2

87.3 ± 0.6

84.7 ± 1.1

 

SVM/RBF

38.2 ± 1.7

80.2 ± 2.2

87.7 ± 0.6

83.9 ± 1.1