Skip to main content

Table 3 Metric values of different models on five-fold cross-validation

From: PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants

Classifiers

Encodings

Sn (%)

Sp (%)

ACC (%)

F1Ë—score (%)

MCC (%)

AUC (%)

RF

aBE

69.4 \(\pm\) 1.12

71.3 \(\pm\) 0.94

70.9 \(\pm\) 0.76

50.1 \(\pm\) 0.99

34.4 \(\pm\) 1.29

77.5 \(\pm\) 0.77

 

AAC

70.5 \(\pm\) 1.89

64.8 \(\pm\) 0.99

66.0 \(\pm\) 0.55

44.6 \(\pm\) 0.78

29.1 \(\pm\) 1.02

74.5 \(\pm\) 0.63

 

EGAAC

70.9 \(\pm\) 1.47

59.3 \(\pm\) 1.44

61.8 \(\pm\) 0.87

43.8 \(\pm\) 0.82

24.7 \(\pm\) 0.67

71.1 \(\pm\) 0.30

 

AAindex

74.1 \(\pm\) 0.83

60.9 \(\pm\) 1.41

63.7 \(\pm\) 0.98

46.2 \(\pm\) 0.58

28.6 \(\pm\) 0.78

75.2 \(\pm\) 0.56

 

BLOSUM62

70.1 \(\pm\) 1.04

66.8 \(\pm\) 0.53

67.5 \(\pm\) 0.30

47.6 \(\pm\) 0.44

30.6 \(\pm\) 0.60

75.5 \(\pm\) 0.50

AdaBoost

BE

24.3 \(\pm\) 1.28

95.4 \(\pm\) 0.17

80.5 \(\pm\) 0.51

34.4 \(\pm\) 1.60

28.6 \(\pm\) 1.82

79.0 \(\pm\) 0.60

 

AAC

17.6 \(\pm\) 0.79

95.3 \(\pm\) 0.28

79.0 \(\pm\) 0.32

26.0 \(\pm\) 0.76

20.1 \(\pm\) 0.52

75.8 \(\pm\) 0.63

 

EGAAC

2.40 \(\pm\) 0.30

99.4 \(\pm\) 0.17

79.0 \(\pm\) 0.28

4.70 \(\pm\) 0.54

7.60 \(\pm\) 1.05

71.5 \(\pm\) 0.66

 

AAindex

25.4 \(\pm\) 0.86

95.3 \(\pm\) 0.36

80.6 \(\pm\) 0.28

35.6 \(\pm\) 1.09

29.5 \(\pm\) 1.43

79.4 \(\pm\) 0.49

 

BLOSUM62

24.2 \(\pm\) 0.79

95.6 \(\pm\) 0.30

80.6 \(\pm\) 0.26

34.4 \(\pm\) 0.80

28.8 \(\pm\) 0.77

78.9 \(\pm\) 0.55

LightGBM

BE

68.6 \(\pm\) 1.30

85.0 \(\pm\) 0.23

81.5 \(\pm\) 0.47

60.9 \(\pm\) 1.00

49.5 \(\pm\) 1.32

85.6 \(\pm\) 0.52

 

AAC

67.3 \(\pm\) 1.03

73.5 \(\pm\) 0.74

72.2 \(\pm\) 0.51

50.4 \(\pm\) 0.66

34.8 \(\pm\) 0.77

78.0 \(\pm\) 0.81

 

EGAAC

70.3 \(\pm\) 0.48

59.4 \(\pm\) 0.85

61.7 \(\pm\) 0.67

43.6 \(\pm\) 0.39

24.3 \(\pm\) 0.75

70.5 \(\pm\) 0.62

 

AAindex

65.1 \(\pm\) 1.13

88.0 \(\pm\) 0.31

83.2 \(\pm\) 0.32

61.9 \(\pm\) 0.81

51.3 \(\pm\) 0.99

86.6 \(\pm\) 0.23

 

BLOSUM62

66.8 \(\pm\) 0.64

87.2 \(\pm\) 0.60

83.0 \(\pm\) 0.50

62.3 \(\pm\) 0.93

51.7 \(\pm\) 1.17

86.9 \(\pm\) 0.54

LSTM

BE

78.3 \(\pm\) 2.23

81.7 \(\pm\) 2.08

81.0 \(\pm\) 1.20

63.4 \(\pm\) 0.86

53.0 \(\pm\) 0.99

88.2 \(\pm\) 0.12

 

WE

74.8 \(\pm\) 0.79

81.8 \(\pm\) 0.47

80.4 \(\pm\) 0.24

61.6 \(\pm\) 0.37

50.3 \(\pm\) 0.36

86.5 \(\pm\) 0.37

 

AAindex

76.6 \(\pm\) 4.05

83.1 \(\pm\) 2.18

81.8 \(\pm\) 0.97

63.8 \(\pm\) 0.94

53.5 \(\pm\) 1.15

88.0 \(\pm\) 0.59

 

BLOSUM62

72.3 \(\pm\) 3.92

84.7 \(\pm\) 2.36

82.1 \(\pm\) 1.18

63.0 \(\pm\) 1.26

52.3 \(\pm\) 1.50

87.6 \(\pm\) 0.43

BiLSTM

BE

76.9 \(\pm\) 3.03

82.1 \(\pm\) 2.71

81.0 \(\pm\) 1.50

63.1 \(\pm\) 1.13

52.6 \(\pm\) 1.22

88.0 \(\pm\) 0.25

 

WE

74.4 \(\pm\) 2.67

81.9 \(\pm\) 130

80.3 \(\pm\) 0.50

61.4 \(\pm\) 0.55

50.1 \(\pm\) 0.62

86.6 \(\pm\) 0.54

 

AAindex

77.6 \(\pm\) 2.28

81.7 \(\pm\) 1.25

80.9 \(\pm\) 0.56

63.0 \(\pm\) 0.55

52.4 \(\pm\) 0.65

88.1 \(\pm\) 0.30

 

BLOSUM62

81.1 \(\pm\) 3.10

78.4 \(\pm\) 2.95

79.0 \(\pm\) 1.70

62.0 \(\pm\) 0.86

51.3 \(\pm\) 1.01

87.7 \(\pm\) 0.31

CNN

BE

80.9 \(\pm\) 1.78

81.5 \(\pm\) 0.92

81.0 \(\pm\) 0.50

64.2 \(\pm\) 0.89

54.1 \(\pm\) 1.04

88.8 \(\pm\) 0.34

 

WE

80.3 \(\pm\) 2.59

81.6 \(\pm\) 1.95

81.4 \(\pm\) 1.13

64.4 \(\pm\) 1.25

54.4 \(\pm\) 1.43

88.6 \(\pm\) 0.47

 

AAindex

78.3 \(\pm\) 4.65

82.6 \(\pm\) 2.94

81.7 \(\pm\) 1.40

64.3 \(\pm\) 0.87

54.2 \(\pm\) 0.90

88.5 \(\pm\) 0.64

 

BLOSUM62

77.3 \(\pm\) 4.29

83.0 \(\pm\) 2.75

81.9 \(\pm\) 1.31

64.2 \(\pm\) 0.82

54.0 \(\pm\) 0.96

88.6 \(\pm\) 0.32

PlantNh-Kcr

BE

82.1 \(\pm\) 2.36

81.0 \(\pm\) 1.91

81.2 \(\pm\) 1.04

64.8 \(\pm\) 0.33

55.1 \(\pm\) 0.54

89.1 \(\pm\) 0.54

  1. aBold indicates the best performance for the classifier