Skip to main content

Table 3 Performance comparison of proposed features with AAC, DPC, PSSM, and the combined features with highest performance values for each class highlighted in bold

From: TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings

 

Cross-validation data

Feature types

Acc (%)

Spec (%)

Sen (%)

MCC

AAC

60.69 ± 12.13

59.61 ± 15.82

68.01 ± 19.77

0.24 ± 0.09

DPC

70.42 ± 13.97

72.89 ± 17.91

51.67 ± 20.14

0.24 ± 0.13

AAC-DPC

86.48 ± 5.82

88.83 ± 8.04

69.34 ± 16.62

0.53 ± 0.08

PSSM

89.57 ± 6.68

91.88 ± 5.73

73.34 ± 18.33

0.61 ± 0.16

PSSM-AAC

91.17 ± 3.18

93.19 ± 4.11

76.34 ± 12.91

0.66 ± 0.1

PSSM-DPC

91.25 ± 3.29

93.85 ± 4.58

72.34 ± 12.58

0.64 ± 0.09

PSSM-DPC-AAC

91.25 ± 2.84

93.67 ± 3.85

73.67 ± 14.45

0.64 ± 0.1

Proposed features

95.82 ± 1.67

97.59 ± 2.15

83.67 ± 7.45

0.83 ± 0.06

 

Independent data

Feature types

Acc (%)

Spec (%)

Sen (%)

MCC

AAC

46.92 ± 25.59

42.37 ± 33.15

81.25 ± 34.49

0.20 ± 0.07

DPC

82.05 ± 23.71

84.63 ± 29.06

62.75 ± 39.92

0.46 ± 0.28

AAC-DPC

93.65 ± 2.66

94.95 ± 3.26

83.75 ± 9.59

0.73 ± 0.09

PSSM

94.85 ± 2.82

97.56 ± 2.85

74.5 ± 28.35

0.72 ± 0.26

PSSM-AAC

95.77 ± 1.45

97.42 ± 2.14

83.25 ± 11.31

0.81 ± 0.06

PSSM-DPC

95.94 ± 1.33

97.81 ± 1.49

82 ± 8.8

0.81 ± 0.07

PSSM-DPC-AAC

95.14 ± 2.02

96.77 ± 2.48

83 ± 9.7

0.78 ± 0.08

Proposed features

96.49 ± 4.34

98 ± 5.27

85 ± 17.48

0.86 ± 0.13

  1. (Each result is reported in format: m ± d, where m is the mean and d is the standard deviation across the ten runs)