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Table 1 The mean values of SVM prediction performance parameters of 100 runs using various kernels

From: Predicting substrates of the human breast cancer resistance protein using a support vector machine method

Kernel

Category

ACC

SE

SP

MCC

Linear

Training

80.7

90.4

64.8

0.581

 

Test

68.8

82.1

46.6

0.312

 

External

70.7

77.6

59.1

0.375

Polynomial

Training

79.2

96.4

50.7

0.506

 

Test

65.8

86.8

30.8

0.198

 

External

66.1

81.9

39.8

0.174

RBF

Training

84.5

93.8

69.1

0.665

 

Test

69.7

83.3

47.2

0.332

 

External

70.9

77.7

59.6

0.382

  1. ACC, accuracy (overall prediction accuracy); SP, specificity (prediction accuracy for the non-substrates); SE, sensitivity (prediction accuracy for the substrates); MCC, the Matthews correlation coefficient (a more balanced prediction parameter than ACC). The external data set was only used to validate the prediction power of the models constructed, and was not used for model selection.