Performance of BVLSTM-MHC along with ten existent MHC class I predictors on independent MHCBN dataset
Methods . | ACC . | AUC . | F1 . | MCC . | Specificity . | Sensitivity . | Precision . | AUPR . | # Postive examples . | # Negative examples . |
---|---|---|---|---|---|---|---|---|---|---|
ANN [44] | 0.8827 | 0.9166 | 0.6866 | 0.6205 | 0.9507 | 0.6216 | 0.7667 | 0.7726 | 37 | 142 |
comblibsidney 2008 [71] | 0.7419 | 0.1141 | — | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.1591 | 16 | 46 |
NetMHCcons [42] | 0.8883 | 0.9169 | 0.7222 | 0.6528 | 0.9366 | 0.7027 | 0.7429 | 0.8457 | 37 | 142 |
NetMHCpan [63] | 0.8547 | 0.9024 | 0.5938 | 0.5173 | 0.9437 | 0.5135 | 0.7037 | 0.7471 | 37 | 142 |
NetMHCpan EL [43] | 0.8156 | 0.8258 | 0.5075 | 0.3989 | 0.9085 | 0.4595 | 0.5667 | 0.5977 | 37 | 142 |
PickPocket [27] | 0.8715 | 0.8429 | 0.6849 | 0.6043 | 0.9225 | 0.6757 | 0.6944 | 0.6378 | 37 | 142 |
SMM [72] | 0.9268 | 0.9508 | 0.7692 | 0.7266 | 0.9635 | 0.7407 | 0.8000 | 0.8548 | 27 | 137 |
SMMPMBEC [25] | 0.9146 | 0.9586 | 0.7308 | 0.6808 | 0.9562 | 0.7037 | 0.7600 | 0.8581 | 27 | 137 |
BVLSTM-MHC | 0.9548 | 0.9512 | 0.8750 | 0.8490 | 0.9832 | 0.8333 | 0.9211 | 0.9112 | 42 | 179 |
CNN-NF [46] | 0.8606 | 0.8513 | 0.6234 | 0.5449 | 0.8908 | 0.7059 | 0.5581 | 0.6383 | 34 | 174 |
MHCflurry [45] | 0.7834 | 0.7526 | 0.4946 | 0.3633 | 0.8352 | 0.5610 | 0.4423 | 0.4569 | 41 | 176 |
Methods . | ACC . | AUC . | F1 . | MCC . | Specificity . | Sensitivity . | Precision . | AUPR . | # Postive examples . | # Negative examples . |
---|---|---|---|---|---|---|---|---|---|---|
ANN [44] | 0.8827 | 0.9166 | 0.6866 | 0.6205 | 0.9507 | 0.6216 | 0.7667 | 0.7726 | 37 | 142 |
comblibsidney 2008 [71] | 0.7419 | 0.1141 | — | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.1591 | 16 | 46 |
NetMHCcons [42] | 0.8883 | 0.9169 | 0.7222 | 0.6528 | 0.9366 | 0.7027 | 0.7429 | 0.8457 | 37 | 142 |
NetMHCpan [63] | 0.8547 | 0.9024 | 0.5938 | 0.5173 | 0.9437 | 0.5135 | 0.7037 | 0.7471 | 37 | 142 |
NetMHCpan EL [43] | 0.8156 | 0.8258 | 0.5075 | 0.3989 | 0.9085 | 0.4595 | 0.5667 | 0.5977 | 37 | 142 |
PickPocket [27] | 0.8715 | 0.8429 | 0.6849 | 0.6043 | 0.9225 | 0.6757 | 0.6944 | 0.6378 | 37 | 142 |
SMM [72] | 0.9268 | 0.9508 | 0.7692 | 0.7266 | 0.9635 | 0.7407 | 0.8000 | 0.8548 | 27 | 137 |
SMMPMBEC [25] | 0.9146 | 0.9586 | 0.7308 | 0.6808 | 0.9562 | 0.7037 | 0.7600 | 0.8581 | 27 | 137 |
BVLSTM-MHC | 0.9548 | 0.9512 | 0.8750 | 0.8490 | 0.9832 | 0.8333 | 0.9211 | 0.9112 | 42 | 179 |
CNN-NF [46] | 0.8606 | 0.8513 | 0.6234 | 0.5449 | 0.8908 | 0.7059 | 0.5581 | 0.6383 | 34 | 174 |
MHCflurry [45] | 0.7834 | 0.7526 | 0.4946 | 0.3633 | 0.8352 | 0.5610 | 0.4423 | 0.4569 | 41 | 176 |
Performance of BVLSTM-MHC along with ten existent MHC class I predictors on independent MHCBN dataset
Methods . | ACC . | AUC . | F1 . | MCC . | Specificity . | Sensitivity . | Precision . | AUPR . | # Postive examples . | # Negative examples . |
---|---|---|---|---|---|---|---|---|---|---|
ANN [44] | 0.8827 | 0.9166 | 0.6866 | 0.6205 | 0.9507 | 0.6216 | 0.7667 | 0.7726 | 37 | 142 |
comblibsidney 2008 [71] | 0.7419 | 0.1141 | — | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.1591 | 16 | 46 |
NetMHCcons [42] | 0.8883 | 0.9169 | 0.7222 | 0.6528 | 0.9366 | 0.7027 | 0.7429 | 0.8457 | 37 | 142 |
NetMHCpan [63] | 0.8547 | 0.9024 | 0.5938 | 0.5173 | 0.9437 | 0.5135 | 0.7037 | 0.7471 | 37 | 142 |
NetMHCpan EL [43] | 0.8156 | 0.8258 | 0.5075 | 0.3989 | 0.9085 | 0.4595 | 0.5667 | 0.5977 | 37 | 142 |
PickPocket [27] | 0.8715 | 0.8429 | 0.6849 | 0.6043 | 0.9225 | 0.6757 | 0.6944 | 0.6378 | 37 | 142 |
SMM [72] | 0.9268 | 0.9508 | 0.7692 | 0.7266 | 0.9635 | 0.7407 | 0.8000 | 0.8548 | 27 | 137 |
SMMPMBEC [25] | 0.9146 | 0.9586 | 0.7308 | 0.6808 | 0.9562 | 0.7037 | 0.7600 | 0.8581 | 27 | 137 |
BVLSTM-MHC | 0.9548 | 0.9512 | 0.8750 | 0.8490 | 0.9832 | 0.8333 | 0.9211 | 0.9112 | 42 | 179 |
CNN-NF [46] | 0.8606 | 0.8513 | 0.6234 | 0.5449 | 0.8908 | 0.7059 | 0.5581 | 0.6383 | 34 | 174 |
MHCflurry [45] | 0.7834 | 0.7526 | 0.4946 | 0.3633 | 0.8352 | 0.5610 | 0.4423 | 0.4569 | 41 | 176 |
Methods . | ACC . | AUC . | F1 . | MCC . | Specificity . | Sensitivity . | Precision . | AUPR . | # Postive examples . | # Negative examples . |
---|---|---|---|---|---|---|---|---|---|---|
ANN [44] | 0.8827 | 0.9166 | 0.6866 | 0.6205 | 0.9507 | 0.6216 | 0.7667 | 0.7726 | 37 | 142 |
comblibsidney 2008 [71] | 0.7419 | 0.1141 | — | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 0.1591 | 16 | 46 |
NetMHCcons [42] | 0.8883 | 0.9169 | 0.7222 | 0.6528 | 0.9366 | 0.7027 | 0.7429 | 0.8457 | 37 | 142 |
NetMHCpan [63] | 0.8547 | 0.9024 | 0.5938 | 0.5173 | 0.9437 | 0.5135 | 0.7037 | 0.7471 | 37 | 142 |
NetMHCpan EL [43] | 0.8156 | 0.8258 | 0.5075 | 0.3989 | 0.9085 | 0.4595 | 0.5667 | 0.5977 | 37 | 142 |
PickPocket [27] | 0.8715 | 0.8429 | 0.6849 | 0.6043 | 0.9225 | 0.6757 | 0.6944 | 0.6378 | 37 | 142 |
SMM [72] | 0.9268 | 0.9508 | 0.7692 | 0.7266 | 0.9635 | 0.7407 | 0.8000 | 0.8548 | 27 | 137 |
SMMPMBEC [25] | 0.9146 | 0.9586 | 0.7308 | 0.6808 | 0.9562 | 0.7037 | 0.7600 | 0.8581 | 27 | 137 |
BVLSTM-MHC | 0.9548 | 0.9512 | 0.8750 | 0.8490 | 0.9832 | 0.8333 | 0.9211 | 0.9112 | 42 | 179 |
CNN-NF [46] | 0.8606 | 0.8513 | 0.6234 | 0.5449 | 0.8908 | 0.7059 | 0.5581 | 0.6383 | 34 | 174 |
MHCflurry [45] | 0.7834 | 0.7526 | 0.4946 | 0.3633 | 0.8352 | 0.5610 | 0.4423 | 0.4569 | 41 | 176 |
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