ArticleNucleic acids research2025
AllergyPred: a web server for allergen prediction.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Deciphering Allergen Peptides for Dermatological and Cosmetic Applications with Explainable Artificial Intelligence.Journal of proteome research · 2026Article
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- Current challenges in allergic diseases and computational solutions towards personalized medicine.Frontiers in allergy · 2026Review
- Prediction of plant food allergens using protein embeddings.Bioinformatics advances · 2026Article
- Multimodal deep learning for allergenic proteins prediction.BMC biology · 2025Article
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
Abstract
Identifying allergenic proteins in raw materials can help reformulate products to make them safer for sensitive populations. Computational tools are powerful for identifying the allergenic potential of proteins and chemicals in food and personal care products. These tools can help minimize allergic risks and guide safer product development by leveraging sequence analysis, structural modelling, and epitope mapping. Food allergens can sometimes impact how a drug is processed or worsen allergic responses. These interactions can pose significant health risks and complicate treatment plans. In addition to this, certain foods can influence how drugs are absorbed, metabolized, or broken down in the body. While not all interactions trigger allergies, they may amplify reactions or side effects. Cross-reactivity occurs when proteins in foods share structural similarities with components in certain drugs, leading the immune system to react to both mistakenly. Here, we present AllergyPred, a web server that predicts both protein- and chemical-based allergens. Five different models take protein IDs, sequences, chemical IDs, and structures as inputs for predicting respective allergy endpoints. The AllergyPred web server is free and open to all users, and there is no login requirement. It can be accessed via https://allergypred.charite.de/AllergyPred/. The prediction results will be presented in the form of a table and can be downloaded in several file formats supporting users to report the results for the respective projects.
Indexed as
Identifiers
What Socratic 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 Socratic graph.