ArticleGenome medicine2025
Predicting MHC-I ligands across alleles and species: how far can we go?
Article in Genome medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Highlight of 2025: From binding prediction to molecular design-computational advances in TCR-pMHC prediction and targeting.Immunology and cell biology · 2026Review
- TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics.Genome biology · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.Bioinformatics (Oxford, England) · 2026Article
- AI-enabled virtual immunopeptidomics links quantitative neoantigen presentation to immunogenicity.bioRxiv : the preprint server for biology · 2026Article
- Identification of MHC Ligands Through Allele-Guided Isolation Combined With Machine Learning for Improved MHC Assignment Using ARDisplay-I.Molecular & cellular proteomics : MCP · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- Trivalent multi-epitope mRNA vaccine against norovirus, rotavirus, and adenovirus 40/41: epitope screening, molecular docking, and molecular dynamics simulation with in silico validation guided by immunoinformatics.Infectious diseases of poverty · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Profiling immunogenic neoantigen peptides elicited by personalized neoantigen vaccine in cancer patients.Frontiers in immunology · 2026Article
- Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction.Frontiers in genetics · 2026Review
- CIMT 2025: Report on the 22Human vaccines & immunotherapeutics · 2025Article
- Harnessing TCR repertoires: predictive insights and therapeutic monitoring in cancer immunotherapy.Immuno-oncology technology · 2025Review
- A machine learning-based method to optimize the immunogenicity of human leukocyte antigen class I-restricted neoantigens.Briefings in bioinformatics · 2025Article
- Identification of T-Cell Epitopes and Vaccine Development for African Swine Fever Virus.Vaccines · 2025Review
- Comprehensive assessment of computational methods for cancer immunoediting.Cell reports methods · 2025Article
- Article
- Immunolyser 2.0: An advanced computational pipeline for comprehensive analysis of immunopeptidomic data.Computational and structural biotechnology journal · 2025Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundCD8
methodsWe capitalize on an expanded architecture of our MHC-I ligand predictor (MixMHCpred3.0) to systematically assess the extent to which predictions of MHC-I ligands can be applied to MHC-I alleles that currently lack known ligand data.
resultsOur results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand prediction accuracy across alleles and species. Robust benchmarking on external data shows that our MHC-I ligand predictor demonstrates competitive performance relative to other state-of-the-art MHC-I ligand predictors and can be used for CD8
conclusionsOur work provides a valuable tool for predicting antigen presentation across all human and mouse MHC-I alleles. MixMHCpred3.0 tool is available at https://github.com/GfellerLab/MixMHCpred .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.