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Table 11 Comparison of the proposed system with existing nine prediction systems in terms of Tyrosine site prediction in the independent dataset.

From: Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Systems

Category

Performance parameters of the systems

  

Ac(%)

Sn(%)

Sp(%)

Mcc

FPR(%)

KinasePhos [11]

 

94.39

2.06

96.06

-0.01

3.94

NetPhosK [10]

Kinase Specific

86.62

12.37

87.97

0.00

12.03

PPSP [6]

 

82.40

13.40

83.66

-0.01

16.34

GPS [15]

 

83.03

14.43

84.28

-0.00

15.72

AutoMotif Server AMS [14]

 

41.47

54.64

41.23

-0.01

58.77

DISPHOS [8]

 

94.07

8.25

95.63

0.02

4.37

NetPhos [13]

 

82.96

16.49

84.16

0.00

15.84

PHOSIDA [5]

Kinase Independent

97.18

3.09

98.89

0.02

1.11

Scansite [12]

 

97.37

3.09

99.08

0.03

0.92

PPRED (Proposed system)

 

64.83

76.29

64.62

0.11

35.38