Table 1.
Algorithm | Brief description of algorithm | Refs. |
---|---|---|
NetMHC-3.0 | Artificial neural network-based algorithm for prediction of binding affinity between MHC-I and peptides of length 8–11 | [89] |
NetMHCpan-3.0 | Machine-learning model-based algorithm for prediction of binding affinity between MHC-I and peptides, a pan-specific version | [90] |
NetMHCcons | Comprehensive algorithm for prediction of binding affinity between MHC-I and peptides | [41] |
NetMHCstab | Artificial neural network-based algorithm for prediction of stability of peptide-MHC-I complex | [91] |
PickPocket | Position-specific scoring matrix-based algorithm for prediction of binding affinity between MHC-I and peptides | [92] |
FRED2 | Epitope prediction for neoantigen | [93] |
NetCTL-1.2 | Comprehensive prediction algorithm containing proteasome cleavage, TAP transport, and MHC-I binding affinity | [94] |
NetCTLpan | The pan-specific version of NetCTL | [95] |
NetTepi | Integrated prediction algorithm containing binding affinity, stability of peptide-MHC-I complex, and T cell propensity | [46] |
pVAC-Seq | Identification of neoantigen by tumor mutation and expression data | [96] |
EpiToolKit | Prediction of MHC-I typing and T cell epitope | [97] |
WAPP | Comprehensive prediction algorithm containing proteasome cleavage, TAP transport, and MHC-I binding affinity | [98] |
MHC major histocompatibility complex, TAP transporter associated with antigen processing