Skip to main content

Table 4 Metric values of different models on independent test

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

BE

69.6 \(\pm\) 0.30

70.7 \(\pm\) 0.41

70.5 \(\pm\) 0.27

49.8 \(\pm\) 0.15

33.9 \(\pm\) 0.22

77.4 \(\pm\) 0.04

 

AAC

70.5 \(\pm\) 0.32

65.1 \(\pm\) 0.20

66.2 \(\pm\) 0.15

46.8 \(\pm\) 0.18

29.4 \(\pm\) 0.28

74.6 \(\pm\) 0.06

 

EGAAC

70.5 \(\pm\) 0.71

60.9 \(\pm\) 0.54

62.9 \(\pm\) 0.29

44.4 \(\pm\) 0.10

25.6 \(\pm\) 0.17

71.1 \(\pm\) 0.01

 

AAindex

74.5 \(\pm\) 0.40

60.0 \(\pm\) 043

63.0 \(\pm\) 0.27

45.9 \(\pm\) 0.11

28.2 \(\pm\) 0.17

75.2 \(\pm\) 0.10

 

BLOSUM62

70.0 \(\pm\) 0.42

66.4 \(\pm\) 0.40

67.1 \(\pm\) 0.27

47.2 \(\pm\) 0.20

30.1 \(\pm\) 0.30

75.4 \(\pm\) 0.11

AdaBoost

BE

23.5 \(\pm\) 0.00

95.4 \(\pm\) 0.00

80.3 \(\pm\) 0.00

33.8 \(\pm\) 0.00

27.8 \(\pm\) 0.00

78.9 \(\pm\) 0.00

 

AAC

18.0 \(\pm\) 0.00

95.6 \(\pm\) 0.00

79.3 \(\pm\) 0.00

26.8 \(\pm\) 0.00

23.1 \(\pm\) 0.00

76.2 \(\pm\) 0.00

 

EGAAC

2.60 \(\pm\) 0.00

99.4 \(\pm\) 0.00

79.0 \(\pm\) 0.00

5.00 \(\pm\) 0.00

7.90 \(\pm\) 0.00

71.5 \(\pm\) 0.00

 

AAindex

25.8 \(\pm\) 0.00

95.2 \(\pm\) 0.00

80.6 \(\pm\) 0.00

35.0 \(\pm\) 0.00

29.5 \(\pm\) 0.00

79.5 \(\pm\) 0.00

 

BLOSUM62

23.1 \(\pm\) 0.00

95.3 \(\pm\) 0.00

80.1 \(\pm\) 0.00

32.9 \(\pm\) 0.00

26.9 \(\pm\) 0.00

79.0 \(\pm\) 0.00

LightGBM

BE

71.9 \(\pm\) 0.00

84.0 \(\pm\) 0.00

81.4 \(\pm\) 0.00

62.0 \(\pm\) 0.00

50.8 \(\pm\) 0.00

86.6 \(\pm\) 0.00

 

AAC

69.0 \(\pm\) 0.00

72.2 \(\pm\) 0.00

71.5 \(\pm\) 0.00

50.5 \(\pm\) 0.00

34.9 \(\pm\) 0.00

78.4 \(\pm\) 0.00

 

EGAAC

72.4 \(\pm\) 0.00

59.0 \(\pm\) 0.00

61.9 \(\pm\) 0.00

44.4 \(\pm\) 0.00

25.7 \(\pm\) 0.00

71.1 \(\pm\) 0.00

 

AAindex

69.4 \(\pm\) 0.00

86.9 \(\pm\) 0.00

83.2 \(\pm\) 0.00

63.5 \(\pm\) 0.00

53.0 \(\pm\) 0.00

87.6 \(\pm\) 0.00

 

BLOSUM62

71.7 \(\pm\) 0.00

86.2 \(\pm\) 0.00

83.2 \(\pm\) 0.00

64.2 \(\pm\) 0.00

53.8 \(\pm\) 0.00

88.1 \(\pm\) 0.00

LSTM

BE

79.7 \(\pm\) 5.22

82.2 \(\pm\) 3.29

81.7 \(\pm\) 1.54

64.7 \(\pm\) 0.66

54.9 \(\pm\) 0.60

89.0 \(\pm\) 0.14

 

WE

75.2 \(\pm\) 1.82

83.4 \(\pm\) 1.32

81.7 \(\pm\) 0.62

63.3 \(\pm\) 0.40

52.7 \(\pm\) 0.51

87.5 \(\pm\) 0.21

 

AAindex

79.0 \(\pm\) 4.48

82.5 \(\pm\) 3.21

81.7 \(\pm\) 1.61

64.6 \(\pm\) 0.77

54.6 \(\pm\) 0.72

88.7 \(\pm\) 0.32

 

BLOSUM62

75.5 \(\pm\) 4.72

83.8 \(\pm\) 2.88

82.0 \(\pm\) 1.33

63.9 \(\pm\) 0.68

53.6 \(\pm\) 0.82

88.5 \(\pm\) 0.43

BiLSTM

BE

75.8 \(\pm\) 3.49

84.3 \(\pm\) 1.77

82.5 \(\pm\) 0.71

64.6 \(\pm\) 0.50

54.5 \(\pm\) 0.67

88.9 \(\pm\) 0.25

 

WE

77.5 \(\pm\) 2.79

81.0 \(\pm\) 0.20

80.3 \(\pm\) 1.03

62.3 \(\pm\) 0.60

51.5 \(\pm\) 0.73

87.4 \(\pm\) 0.28

 

AAindex

79.3 \(\pm\) 3.45

82.2 \(\pm\) 2.59

81.6 \(\pm\) 1.37

64.5 \(\pm\) 0.88

54.5 \(\pm\) 0.96

88.9 \(\pm\) 0.26

 

BLOSUM62

75.5 \(\pm\) 7.16

83.5 \(\pm\) 4.16

81.8 \(\pm\) 1.81

63.7 \(\pm\) 0.58

53.5 \(\pm\) 0.50

88.7 \(\pm\) 0.13

CNN

BE

82.1 \(\pm\) 1.08

82.2 \(\pm\) 0.80

82.1 \(\pm\) 0.43

66.0 \(\pm\) 0.33

56.5 \(\pm\) 0.38

89.6 \(\pm\) 0.07

 

WE

79.0 \(\pm\) 2.13

83.4 \(\pm\) 1.33

82.4 \(\pm\) 0.54

65.4 \(\pm\) 0.48

55.6 \(\pm\) 0.63

89.1 \(\pm\) 0.16

 

AAindex

82.1 \(\pm\) 3.27

81.2 \(\pm\) 2.06

81.4 \(\pm\) 0.96

65.0 \(\pm\) 0.39

55.3 \(\pm\) 0.38

89.1 \(\pm\) 0.14

 

BLOSUM62

79.2 \(\pm\) 4.92

83.1 \(\pm\) 2.88

82.3 \(\pm\) 1.26

65.3 \(\pm\) 0.43

55.6 \(\pm\) 0.38

89.4 \(\pm\) 0.11

PlantNh-Kcr

BE

81.1 \(\pm\) 3.23

83.3 \(\pm\) 2.09

82.8 \(\pm\) 0.99

66.5 \(\pm\) 0.50

57.2 \(\pm\) 0.50

89.9 \(\pm\) 0.19

  1. aBold indicates the best performance for the classifier