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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.