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Table 4 Performance comparison with individual and combined feature encoding schemes for pT site prediction on the independent dataset

From: Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

Attributes Methods Accuracy (%) Sensitivity (%) Specificity (%) Precision (%) F-measure (%) MCC AUC
Physiochemical property RF 77.54 92.1 47.1 78.5 84.7 0.45 0.76
SVM 67.05 77.7 55.9 64.9 70.7 0.34 0.66
Structure RF 89.58 94.8 78.8 90.3 92.5 0.75 0.96
SVM 77.66 99 33 75.6 85.7 0.47 0.66
Sequence RF 71.79 86 42.1 75.7 80.5 0.31 0.69
SVM 73.74 94.4 69.5 70.3 79.9 0.16 0.57
Functional features RF 72.75 92.1 32.3 74.0 82.1 0.31 0.63
SVM 72.43 93.0 29.3 73.4 82.0 0.29 0.61
Functional annotation RF 68.5 92.6 18.1 70.3 79.9 0.16 0.57
SVM 68.34 92.3 31.7 70.3 79.8 0.15 0.55
Combined RF 90.28 97.8 74.4 88.9 93.2 0.77 0.97
SVM 73.96 100 19.4 72.2 83.9 0.37 0.59
  1. Performance metrics for best results are highlighted in bold