ArticleeLife2023
A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.Cell systems · 2026Article
- Scaling antibody language models improves structure aware representation for antibody engineering.Communications biology · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- High-throughput screening for class I peptide MHC binding via yeast surface display.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- T cell receptor specificity landscape revealed through de novo peptide design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- T-cell receptor specificity landscape revealed through de novo peptide design.bioRxiv : the preprint server for biology · 2025Article
- Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- The Evolving T Cell Receptor Recognition Code: The Rules Are More Like Guidelines.Immunological reviews · 2025Review
- Inference of annealed protein fitness landscapes with AnnealDCA.PLoS computational biology · 2024Article
- Review
- Quantification of heterogeneity in human CD8Frontiers in immunology · 2024Article
- TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
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Registered trials
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.