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Table 5 Performance comparison with individual and combined feature encoding schemes for pY 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 77.19 77.5 46 99.4 87.1 0.05 0.65
SVM 79.21 79.6 30.2 99.2 88.4 0.02 0.54
Structure RF 99.3 100 79.4 99.3 99.7 0.43 0.95
SVM 96.08 96.4 63.5 99.7 98 0.27 0.79
Sequence RF 69.74 70 41.3 99 82 0.02 0.59
SVM 99 99.8 11.1 99.2 99.5 0.18 0.55
Functional features RF 98.09 98.6 39.7 99.5 99.0 0.27 0.70
SVM 97.73 98.2 39.7 99.5 98.9 0.24 0.69
Functional annotation RF 95.51 96.1 31.7 99.4 97.7 0.12 0.68
SVM 95.24 95.8 31.7 99.4 97.6 0.12 0.63
Combined RF 99.42 100 63.5 99.5 99.7 0.57 0.99
SVM 99.46 99.7 23.8 99.7 99.7 0.71 0.87
  1. Performance metrics for best results are highlighted in bold