ArticleNature communications2025
Identifying T cell antigen at the atomic level with graph convolutional network.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Current landscape and future directions of neoantigen vaccines: A new era of personalized cancer immunotherapy.Innovation (Cambridge (Mass.)) · 2026Review
- Engineering the next generation of cellular therapies for solid tumors: multi-specific armored CARs and TME reprogramming strategies.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Computational identification of antigen-specific T cell groups through generative epitope modeling.iScience · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Mitigating negative data bias to enhance TCR-epitope binding and residue interaction prediction.Briefings in bioinformatics · 2026Article
- Immune decoding from a multi-omics perspective: Redefining pancreatic cancer tumor microenvironment.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences.Bioinformatics (Oxford, England) · 2026Article
- Bacterial Outer Membrane Vesicles in Potentiating Cancer Vaccines: Progress and Prospects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions.bioRxiv : the preprint server for biology · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Intratumoral TLR4 agonist therapy elicits antigen-specific T cell clonal expansion in metastatic leiomyosarcoma: a case series.Frontiers in oncology · 2026Article
- TCRdesign: an antigen-specific generative language model for de novo design of T-cell receptors.Briefings in bioinformatics · 2025Article
- Reusability Report: Meta-Learning for Antigen-Specific T-Cell Receptor Binder Identification.Research square · 2025Article
- The role of bioinformatics algorithms in modern biopharmaceutical design: Progress, challenges, and future perspectives.BioImpacts : BI · 2025Article
- NeoTImmuML: a machine learning-based prediction model for human tumor neoantigen immunogenicity.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Precise identification of T cell antigens in silico is crucial for the development of cancer mRNA vaccines. However, current computational methods only utilize sequence-level rather than atomic level features to identify T cell antigens, which results in poor representation of those that activate immune responses. Here we propose deepAntigen, a graph convolutional network-based framework, to identify T cell antigens at the atomic level. deepAntigen achieves excellent performance both in the prediction of antigen-human leukocyte antigen (HLA) binding and antigen-T cell receptor (TCR) interactions, which can provide comprehensive guidance for identification of T cell antigens. The tumor neoantigens predicted by deepAntigen in lung, breast and pancreatic cancer patients are experimentally validated through ELISPOT assays, which detect successful activation of CD8
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