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Table 1 Performance comparison between EDLMFC and other ncRPI prediction methods on RPI1807, NPInter v2.0, and RPI488

From: EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction

Dataset

Method

ACC (%)

TPR (%)

TNR (%)

PPV (%)

F1 (%)

MCC (%)

AUC (%)

RPI1807

EDLMC

93.8 ± 0.3

96.9 ± 0.3

84.5 ± 0.9

94.9 ± 0.3

95.9 ± 0.2

83.3 ± 0.8

96.7 ± 0.3

 

RPITER

93.5 ± 0.4

97.1 ± 0.4

82.7 ± 1.1

94.3 ± 0.3

95.7 ± 0.2

82.4 ± 1.0

97.7 ± 0.3

 

IPMinter

93.5 ± 0.3

99.2 ± 0.4

76.8 ± 2.4

92.7 ± 0.7

95.8 ± 0.2

82.6 ± 0.9

88.0 ± 1.0

 

CFRP

92.8 ± 0.4

97.6 ± 0.4

77.4 ± 0.6

92.7 ± 0.3

95.2 ± 0.3

79.7 ± 0.9

96.4 ± 0.1

NPInter v2.0

EDLMFC

89.7 ± 0.2

91.7 ± 0.4

87.7 ± 0.4

88.2 ± 0.3

89.9 ± 0.2

79.5 ± 0.4

95.9 ± 0.2

 

RPITER

89.0 ± 0.6

91.6 ± 0.6

86.2 ± 0.1

87.0 ± 0.8

89.3 ± 0.6

78.1 ± 1.2

95.7 ± 0.4

 

IPMinter

82.8 ± 1.0

84.3 ± 0.9

81.3 ± 2.6

81.3 ± 1.3

83.2 ± 0.9

65.6 ± 2.0

82.7 ± 1.0

 

CFRP

82.1 ± 0.3

77.2 ± 0.5

86.9 ± 0.3

85.5 ± 0.3

81.1 ± 0.3

64.4 ± 0.5

88.4 ± 0.2

RPI488

EDLMC

86.1 ± 0.5

74.5 ± 0.8

96.7 ± 0.5

96.1 ± 0.4

82.9 ± 0.6

74.2 ± 0.9

89.9 ± 0.3

 

RPITER

86.0 ± 1.0

75.1 ± 1.1

95.6 ± 1.9

95.3 ± 1.8

82.9 ± 1.1

74.0 ± 1.9

88.5 ± 0.7

 

IPMinter

79.9 ± 0.8

84.6 ± 0.9

78.6 ± 1.9

79.4 ± 1.3

79.5 ± 0.9

63.7 ± 1.6

81.6 ± 0.9

 

CFRP

79.9 ± 2.0

75.3 ± 1.5

85.3 ± 2.7

82.2 ± 2.8

77.1 ± 2.3

60.8 ± 4.3

86.0 ± 1.8

  1. The values in bold indicate this performance metric is the best among the four methods
  2. The mathematical notation (±) represents standard deviation