ArticlePLoS computational biology2024
A modular protein language modelling approach to immunogenicity prediction.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- Neoantigen Targeting as a Novel Approach for Therapy-Resistant Tumors.Molecular diagnosis & therapy · 2026Review
- Defects in antigen processing and presentation: mechanisms, immune evasion and implications for cancer vaccine development.Nature reviews. Immunology · 2026Review
- Enhancing protein immunogenicity prediction via uncertainty weighted deep ensemble.Oxford open immunology · 2026Article
- AI-driven genotype-phenotype modeling: a framework integrating multi-modal single-cell genomics and reverse vaccinology forFrontiers in genetics · 2026Review
- ImmUQBench: a benchmark on uncertainty quantification of protein immunogenicity prediction.Oxford open immunology · 2026Article
- Artificial intelligence-driven circRNA vaccine development: multimodal collaborative optimization and a new paradigm for biomedical applications.Briefings in bioinformatics · 2025Article
- NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.Frontiers in immunology · 2025Article
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Authors and funding
16 authors.
Funding
Abstract
Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX's encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets.
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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.