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Table 4 The performance on the Gram-negative datasets

From: SigUNet: signal peptide recognition based on semantic segmentation

Method

MCC (%)

FPRTM (%)

Precision (%)

Recall (%)

F1 measure (%)

The SignalP dataset

 Phobius

59.9

22.6

43.9

94.2

59.9

 PrediSi

30.6

69.0

19.7

86.5

32.1

 SignalP3.0-HMM

47.7

39.2

31.6

93.3

47.2

 SignalP3.0-NN

36.7

61.0

22.1

95.2

35.9

 PolyPhobius

60.7

21.4

45.0

94.2

60.9

 Philius

65.9

14.9

51.3

94.2

66.4

 SPOCTOPUS

64.7

17.0

50.8

92.3

65.5

 SignalP 4.0

84.8

1.5

 TOPCONS2

70.8

13.2

57.2

95.2

71.5

 DeepSig

81.2

1.7

88.9

76.9

82.5

 SigUNet

80.6

1.5

88.8

76.0

81.9

The SPDS17 dataset

 Phobius

69.5

18.0

56.4

95.7

71.0

 PrediSi

35.4

66.3

25.0

87.0

38.8

 SignalP3.0-HMM

65.4

21.3

51.2

95.7

66.7

 SignalP3.0-NN

49.1

44.9

33.8

95.7

50.0

 PolyPhobius

75.9

13.5

62.2

100.0

76.7

 Philius

88.7

2.2

84.6

95.7

89.8

 SPOCTOPUS

62.5

20.2

50.0

91.3

64.6

 SignalP 4.0

92.5

0.0

100.0

87.0

93.0

 TOPCONS2

85.9

5.6

76.7

100.0

86.8

 DeepSig

95.0

0.0

100.0

91.3

95.5

 SigUNet

97.6

1.1

95.8

100.0

97.9

  1. The best performance is highlighted in bold