ReviewJournal of translational medicine2026
B-cell epitope prediction in the age of machine learning: advancements and challenges.
Review in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Toward mechanistic virtual immune cells.Nature biotechnology · 2026Article
- Structural vaccinology expedites rational design of next-generation vaccines for influenza and respiratory syncytial virus.Virologica Sinica · 2026Review
- The immunogenicity database collaborative: a standardized, publicly available database for clinical immunogenicity observations and insights.Frontiers in immunology · 2026Article
- Advances in linear epitope-based subunit vaccines powered by artificial intelligence: current status and challenges.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of B-cell epitopes, the regions that bind to antibodies, is essential for creating effective prophylactic treatments against infectious diseases and cancer, particularly in the realm of reverse vaccinology. While experimental techniques like X-ray crystallography and peptide arrays help identify epitopes, they are expensive, time-consuming and differ in throughput and precision.
methodsThis review examines how predictive techniques and datasets have evolved for the problem, highlighting recent breakthroughs in data-driven algorithms used to predict B-cell epitopes. We specifically examine how methodologies have progressed from traditional machine learning to cutting-edge deep learning models. CONCLSION: The review summarizes significant research contributions in this domain including linear and conformational epitope prediction techniques, addresses methodological biases, dataset limitations, systematic evaluation challenges that plague the field, and explores future opportunities for innovation.
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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.