Table 1.

Algorithm for neoantigen prediction

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