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Table 10 Overall performance comparison of our method with the existing web tools for predicting plastid proteins.

From: Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

Tools

Sensitivity

(%)

Specificity

(%)

Accuracy

(%)

MCC

Precision (%)

RFP (%)

WoLF PSORT

56.96

74.76

65.82

0.3223

69.50

30.50

TargetP

55.70

85.89

65.97

0.3998

88.44

11.56

iLoc-PLant

36.39

98.42

67.41

0.4438

95.83

4.17

YLoc (HighRes)

34.81

97.47

66.14

0.4142

93.22

6.78

PLpred (DIPEP)

60.44

92.72

76.58

0.56

89.25

10.75

PLpred (NCC)

65.82

87.97

76.90

0.55

84.55

15.45

  1. Performance comparison done on an 'independent dataset' that contains 316 plastid and 316 non-plastid proteins. MCC = Matthews Correlation Coefficient, RFP = Rate of False Predictions, DIPEP = Dipeptide composition-based classifier, NCC = Nterminal-Center-Cterminal composition-based classifier.
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BMC Bioinformatics

ISSN: 1471-2105

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