MHCRoBERTa: pan-specific peptide–MHC class I binding prediction through transfer learning with label-agnostic protein sequences

ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks

(2019) Yan Hu et al.   BIOINFORMATICS

MHCflurry: Open-Source Class I MHC Binding Affinity Prediction

(2018) Timothy J. O'Donnell et al.   Cell Systems

Identifying neoantigens for use in immunotherapy

(2018) Sharon Hutchison et al.   MAMMALIAN GENOME

UniProt: a worldwide hub of protein knowledge

(2018)   NUCLEIC ACIDS RESEARCH

NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

(2017) Vanessa Jurtz et al.   JOURNAL OF IMMUNOLOGY

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

(2016) Morten Nielsen et al.   Genome Medicine

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

(2015) Massimo Andreatta et al.   BIOINFORMATICS

Neoantigens in cancer immunotherapy

(2015) T. N. Schumacher et al.   SCIENCE

Mutational heterogeneity in cancer and the search for new cancer-associated genes

(2013) Michael S. Lawrence et al.   NATURE

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

(2011) Edita Karosiene et al.   IMMUNOGENETICS

The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding

(2009) Hao Zhang et al.   BIOINFORMATICS

Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior

(2009) Yohan Kim et al.   BMC BIOINFORMATICS

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11

(2008) Claus Lundegaard et al.   NUCLEIC ACIDS RESEARCH