Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology
Author:
Funder
Konsorcjum PL-Grid
Narodowe Centrum Badań i Rozwoju
European Commission
European Regional Development Fund
PCSS
Publisher
Elsevier BV
Reference26 articles.
1. Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution;Jiang;Brief. Bioinform.,2021
2. NNAlign_ma; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions;Alvarez;Mol. Cell. Proteomics,2019
3. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data;Jurtz;J. Immunol.,2017
4. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data;Reynisson;Nucleic Acids Res.,2020
5. MHCflurry: open-source class I MHC binding affinity prediction;O'Donnell;Cell Syst.,2018
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1. Editorial for methods special issue: Big data in digital health: methods, analysis and prospects;Methods;2024-09
2. GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction;Frontiers in Genetics;2024-07-10
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