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Table 3 Performance comparison with individual and combined feature encoding schemes for pS 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 71.5 79 62.3 72.1 75.4 0.42 0.74
SVM 64.31 79.9 45.0 64.2 71.2 0.26 0.62
Structure RF 87.34 86.5 80.6 90.2 88.3 0.74 0.94
SVM 70.12 96.4 37.7 65.6 78.1 0.43 0.67
Sequence RF 70.3 80.1 58.2 70.3 74.9 0.39 0.73
SVM 77.65 99.1 51.2 77.5 83.1 0.59 0.75
Functional features RF 62.87 93.9 24.6 60.6 73.6 0.26 0.59
SVM 62.58 93.4 24.6 60.5 73.4 0.25 0.59
Functional annotation RF 62.75 90.7 28.2 60.9 72.9 0.24 0.60
SVM 62.50 93.6 24.1 60.4 73.4 0.25 0.58
Combined RF 89.16 89.4 88.9 90.8 90.1 0.78 0.95
SVM 88.50 79.9 99.1 99.1 88.5 0.79 0.89
  1. Performance metrics for best results are highlighted in bold