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Table 6 Performance results between RP-FIRF model and the other methods on human dataset

From: Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Model

Acc (%)

Sp (%)

Sen (%)

MCC (%)

AUC

SLIPPER [6]

91.10

95.06

47.26

41.97

0.8723

DXECPPI [25]

30.90

25.83

87.08

8.25

0.5806

PPIevo [26]

78.04

25.82

87.83

20.82

0.7329

LocFuse [27]

80.66

80.50

50.83

20.26

0.7087

CRS [28]

91.54

96.72

34.17

36.33

0.8196

SPAR [28]

92.09

97.40

33.33

38.36

0.8229

Proposed method

97.89

100.00

74.46

85.31

0.8842