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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.