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Table 8 Identifying enhancers (First layer) and their strengths (Second layer) in the independent test datasets compared to other existing methods

From: iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework

Stages

Method

Acc(%)

Sn(%)

Sp(%)

MCC

First layer

iEnhancer-2L

73.00

75.00

71.00

0.4604

EnhancerPred

74.00

73.50

74.50

0.4800

iEnhancer-EL

74.75

71.00

78.50

0.4964

iEnhancer-ECNN

76.90

78.50

75.20

0.5370

iEnhancer-XG

75.75

74.00

77.50

0.5150

iEnhancer-EBLSTM

77.20

75.50

79.50

0.5340

iEnhancer-DCLA

78.25

78.00

78.50

0.5650

Second layer

iEnhancer-2L

60.50

47.00

74.00

0.2181

EnhancerPred

55.00

45.00

65.00

0.1021

iEnhancer-EL

61.00

54.00

68.00

0.2222

iEnhancer-ECNN

67.80

79.10

56.40

0.3680

iEnhancer-XG

63.50

70.00

57.00

0.2720

iEnhancer-EBLSTM

65.80

81.20

53.60

0.3240

iEnhancer-DCLA

78.00

87.00

69.00

0.5693

  1. The highest value achieved on each metric is marked in bold