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Table 7 Overall performance of various feature classifiers on an 'independent test' dataset for the identification of plastid vs. non-plastid proteins (phase-I)

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

Feature type

Sensitivity

(%)

Specificity

(%)

Accuracy

(%)

MCC

Precision (%)

RFP (%)

AAC

69.30

87.03

78.16

0.57

84.23

15.77

PseAA

68.35

87.34

77.85

0.57

84.38

15.62

Dipep

60.44

92.72

76.58

0.56

89.25

10.75

NCC

65.82

87.97

76.90

0.55

84.55

15.45

Phys-Chem

68.35

84.49

76.42

0.54

81.51

18.49

  1. *individual performance of these classifiers can be seen in supplementary material; AAC = amino acid composition (best values at ≥ 0.0 threshold), PseAA = Pseudo amino acid composition (best values at ≥ 0.1 threshold), Dipep = Dipeptide composition (best values at ≥ 0.2 threshold), NCC = Nterminal-Center-Cterminal composition (sequence divided into 3 parts), Phys-Chem = Protein physicochemical properties, MCC = Matthews Correlation Coefficient, RFP = Rate of False Predictions.
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BMC Bioinformatics

ISSN: 1471-2105

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