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Table 8 The performances of the proposed model on the independent datasets

From: N6-methyladenine identification using deep learning and discriminative feature integration

Species

Methods

ACC (%)

SP (%)

SN (%)

MCC

F. vesca

i6mA-Fuse [13]

93.70

94.80

92.80

0.869

6 mA-stack [14]

95.10

97.11

91.06

0.880

Proposed Deep-N6mA

95.65

97.72

93.61

0.892

R. chinensis

i6mA-Fuse [13]

92.90

94.30

91.5

0.858

6 mA-stack [14]

93.44

92.81

94.12

0.868

Proposed Deep-N6mA

94.23

95.32

93.14

0.876