Skip to main content

Table 6 Performance comparison of different existing tools for pS/pT/pY site prediction

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

Phosphorylation site Methods Sensitivity (%) Specificity (%) MCC AUC
Serine PhosPred-RF 79.70 75.00 0.54 0.85
PhosphoSVM 44.43 94.04 0.29 0.84
PPRED 32.27 91.6 0.16 0.75
iPhos-PseEn 79.64 79.78 0.39
Our RF model 89.4 88.9 0.78 0.95
Threonine PhosPred-RF 73.80 72.60 0.46 0.81
PhosphoSVM 37.31 94.99 0.25 0.81
PPRED 34.32 83.65 0.09 0.65
iPhos-PseEn 71.51 80.68 0.34
Our RF model 97.8 74.4 0.77 0.97
Tyrosine PhosPred-RF 72.70 64.00 0.36 0.76
PhosphoSVM 41.92 87.34 0.20 0.73
PPRED 43.04 82.65 0.16 0.70
iPhos-PseEn 76.18 76.29 0.32
Our RF model 100 63.5 0.57 0.99
  1. Performance metrics for best results are highlighted in bold