ArticleFrontiers in oncology2022
A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 17 citations in OpenAlex.
- A bottleneck-first evaluation of antigen delivery platforms for therapeutic cancer vaccines.Materials today. Bio · 2026Review
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.Research (Washington, D.C.) · 2026Article
- In silico design of a multi-epitope vaccine against the triple negative breast cancer.Scientific reports · 2025Article
- Neoantigen-Driven Immunotherapy in Triple-Negative Breast Cancer: Emerging Strategies and Clinical Potential.Biomedicines · 2025Review
- EpiMII: Integrating Structure and Graph Neural Networks for MHC-II Epitope and Neoantigen Design.bioRxiv : the preprint server for biology · 2025Article
- Large-scale transcript variants dictate neoepitopes for cancer immunotherapy.Science advances · 2025Article
- T cell assays for non-clinical immunogenicity risk assessment: best practices recommended by the European Immunogenicity Platform.Frontiers in immunology · 2025Review
- Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies.Vaccines · 2024Article
- An integrated database of experimentally validated major histocompatibility complex epitopes for antigen-specific cancer therapy.Antibody therapeutics · 2024Article
- Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence.European journal of medical research · 2024Review
- Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for theFrontiers in veterinary science · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- The Value of Microbes in Cancer Neoantigen Immunotherapy.Pharmaceutics · 2023Review
- Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past decade, the substantial achievements of therapeutic cancer vaccines have shed a new light on cancer immunotherapy. The major challenge for designing potent therapeutic cancer vaccines is to identify neoantigens capable of inducing sufficient immune responses, especially involving major histocompatibility complex (MHC)-II epitopes. However, most previous studies on T-cell epitopes were focused on either ligand binding or antigen presentation by MHC rather than the immunogenicity of T-cell epitopes. In order to better facilitate a therapeutic vaccine design, in this study, we propose a revolutionary new tool: a convolutional neural network model named FIONA (Flexible Immunogenicity Optimization Neural-network Architecture) trained on IEDB datasets. FIONA could accurately predict the epitopes presented by the given specific MHC-II subtypes, as well as their immunogenicity. By leveraging the human leukocyte antigen allele hierarchical encoding model together with peptide dense embedding fusion encoding, FIONA (with AUC = 0.94) outperforms several other tools in predicting epitopes presented by MHC-II subtypes in head-to-head comparison; moreover, FIONA has unprecedentedly incorporated the capacity to predict the immunogenicity of epitopes with MHC-II subtype specificity. Therefore, we developed a reliable pipeline to effectively predict CD4+ T-cell immune responses against cancer and infectious diseases.
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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.