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Table 6 Overall performance of various feature classifiers in 5-fold cross-validation 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 (%)

SVM kernel type

AAC

85.37

85.65

85.51

0.71

85.61

14.39

RBF (γ = 370, C = 3, j = 1)

PseAA

89.45

82.95

86.20

0.73

83.99

16.01

RBF (γ = 385, C = 2, j = 2)

Dipep

88.08

85.51

86.80

0.74

85.88

14.12

RBF (γ = 265, C = 6, j = 1)

NCC

84.14

89.66

86.90

0.74

89.06

10.94

RBF (γ = 20, C = 3, j = 2)

Phys-Chem

79.57

81.05

80.31

0.61

80.76

19.24

RBF (γ = 135, C = 2, j = 1)

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