Table 4 Performance comparison of the Met-predictor with other existing methods on the independent test set II.
From: Two-Level Protein Methylation Prediction using structure model-based features
Residue | Methods | MCC | ACC | SEN | SPE | PRE | CPRE | AUC | PRAUC |
|---|---|---|---|---|---|---|---|---|---|
K | MASA | 0.014 | 0.501 | 0.010 | 0.992 | 0.574 | 0.057 | — | — |
PLMLA | 0.067 | 0.533 | 0.540 | 0.527 | 0.533 | 0.048 | — | — | |
PmeS | −0.018 | 0.499 | 0.004 | 0.993 | 0.382 | 0.027 | — | — | |
MethK | 0.007 | 0.500 | 0.001 | 1.000 | 0.667 | 0.082 | — | — | |
iLM_2L | −0.005 | 0.498 | 0.213 | 0.783 | 0.495 | 0.042 | — | — | |
     MONO | −0.018 | 0.493 | 0.200 | 0.786 | 0.483 | 0.996 | — | — | |
     DI | Nan | 0.500 | 0.000 | 1.000 | Nan | Nan | — | — | |
     TRI | 0.258 | 0.625 | 0.500 | 0.750 | 0.667 | 0.002 | — | — | |
GPS-MSP | −0.010 | 0.499 | 0.002 | 0.997 | 0.400 | 0.029 | — | — | |
     MONO | 0.004 | 0.500 | 0.001 | 0.999 | 0.571 | 0.997 | — | — | |
     DI | Nan | 0.500 | 0.000 | 1.000 | Nan | Nan | — | — | |
     TRI | Nan | 0.500 | 0.000 | 1.000 | Nan | Nan | — | — | |
Met-predictor(seq) | 0.035 | 0.517 | 0.508 | 0.526 | 0.518 | 0.046 | 0.520 | 0.513 | |
     MONO | 0.006 | 0.503 | 0.488 | 0.518 | 0.503 | 0.996 | 0.506 | 0.500 | |
     DI | 0.000 | 0.500 | 0.364 | 0.636 | 0.500 | 0.004 | 0.504 | 0.512 | |
     TRI | 0.258 | 0.625 | 0.500 | 0.750 | 0.667 | 0.002 | 0.625 | 0.748 | |
Met-predictor(seq + str) | 0.109 | 0.554 | 0.532 | 0.576 | 0.557 | 0.053 | 0.566 | 0.540 | |
     MONO | 0.094 | 0.545 | 0.412 | 0.678 | 0.562 | 0.997 | 0.560 | 0.538 | |
     DI | 0.218 | 0.545 | 0.091 | 1.000 | 1.000 | 1.000 | 0.570 | 0.547 | |
     TRI | 0.577 | 0.750 | 0.500 | 1.000 | 1.000 | 1.000 | 0.625 | 0.748 | |
R | MASA | Nan | 0.500 | 0.000 | 1.000 | Nan | Nan | — | — |
PmeS | 0.070 | 0.516 | 0.074 | 0.958 | 0.640 | 0.064 | — | — | |
GPS-MSP | 0.113 | 0.531 | 0.115 | 0.948 | 0.688 | 0.078 | — | — | |
     MONO | 0.232 | 0.586 | 0.249 | 0.923 | 0.764 | 0.252 | — | — | |
     DI | 0.091 | 0.534 | 0.207 | 0.862 | 0.600 | 0.092 | — | — | |
Met-predictor(seq) | 0.166 | 0.583 | 0.634 | 0.532 | 0.575 | 0.050 | 0.640 | 0.666 | |
     MONO | 0.370 | 0.680 | 0.793 | 0.568 | 0.647 | 0.161 | 0.749 | 0.736 | |
     DI | 0.044 | 0.506 | 0.023 | 0.989 | 0.667 | 0.119 | 0.530 | 0.528 | |
Met−predictor(seq + str) | 0.262 | 0.618 | 0.404 | 0.833 | 0.707 | 0.085 | 0.643 | 0.677 | |
     MONO | 0.412 | 0.645 | 0.290 | 1.000 | 1.000 | 1.000 | 0.705 | 0.727 | |
     DI | 0.132 | 0.517 | 0.034 | 1.000 | 1.000 | 1.000 | 0.632 | 0.606 |
