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Table 3 The performance on the Gram-positive datasets

From: SigUNet: signal peptide recognition based on semantic segmentation

Method

MCC (%)

FPRTM (%)

Precision (%)

Recall (%)

F1 measure (%)

The SignalP dataset

 Phobius

67.7

20.5

60.0

87.5

71.2

 PrediSi

40.9

54.7

35.0

75.0

47.7

 SignalP3.0-HMM

55.8

43.6

44.3

89.6

59.3

 SignalP3.0-NN

47.2

56.4

34.9

91.7

50.6

 PolyPhobius

71.1

16.2

66.1

85.4

74.5

 Philius

69.6

15.4

64.1

85.4

73.2

 SPOCTOPUS

73.9

15.4

67.2

89.6

76.8

 SignalP 4.0

85.1

2.6

 TOPCONS2

81.6

6.8

80.8

87.5

84.0

 DeepSig

73.9

6.8

81.4

72.9

76.9

 SigUNet

76.1

5.1

85.4

72.9

78.7

The SPDS17 dataset

 Phobius

35.0

13.6

17.9

77.8

29.2

 PrediSi

14.3

64.0

5.0

77.8

9.5

 SignalP3.0-HMM

27.3

27.0

11.9

77.8

20.6

 SignalP3.0-NN

16.1

45.5

5.7

77.8

10.7

 PolyPhobius

34.5

13.2

17.5

77.8

28.6

 Philius

30.3

79.0

16.2

66.7

26.1

 SPOCTOPUS

30.3

13.8

16.2

66.7

26.1

 SignalP 4.0

50.3

0.0

40.0

66.7

50.0

 TOPCONS2

38.1

4.2

24.0

66.7

35.3

 DeepSig

54.5

0.1

46.2

66.7

54.4

 SigUNet

40.9

2.1

40.0

44.4

42.1

  1. The best performance is highlighted in bold