ArticleOxford open immunology2026
ImmUQBench: a benchmark on uncertainty quantification of protein immunogenicity prediction.
Article in Oxford open immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A bottleneck-first evaluation of antigen delivery platforms for therapeutic cancer vaccines.Materials today. Bio · 2026Review
- Optimizing protein design through uncertainty-weighted steering of protein language models.Bioinformatics (Oxford, England) · 2026Article
- An integrated computational antigen discovery pipeline with hierarchical filtering for emerging viral variants.NAR molecular medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discovering antigen proteins, capable of eliciting desired immune responses, is of paramount importance in developing immunogenic therapeutics for combating various diseases, particularly autoimmune disorders, infectious diseases, as well as cancers. Despite recent advances in artificial intelligence (AI) and machine learning (ML), accurate and generalizable immunogenicity prediction remains challenging due to limited labeled data and model over-simplifications. Uncertainty quantification (UQ) approaches are commonly used to address the aforementioned challenges when applying AI/ML methods with limited training data, aiming to reduce the risk of catastrophic errors. This study aims to systematically evaluate the performance of UQ methods for antigen immunogenicity prediction and to establish a benchmark for assessing model reliability in data-scarce setting. We here present
Indexed as
Identifiers
What OpenQuestion holds
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.