ArticleNucleic acids research2006
Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 42 citations in OpenAlex.
- Recent insights for fish allergens: novel processing techniques and detection technology.Journal of the science of food and agriculture · 2026Review
- Deciphering Allergen Peptides for Dermatological and Cosmetic Applications with Explainable Artificial Intelligence.Journal of proteome research · 2026Article
- Multimodal deep learning for allergenic proteins prediction.BMC biology · 2025Article
- Enhancing allergenicity risk assessment for novel foods in the EU: insights from the updated EFSA guidance - requirements, knowledge gaps and research needs.Frontiers in toxicology · 2025Review
- Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.ACS omega · 2023Article
- Computational prediction of allergenic proteins based on multi-feature fusion.Frontiers in genetics · 2023Article
- A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins.Foods (Basel, Switzerland) · 2021Article
- Origin and Functional Prediction of Pollen Allergens in Plants.Plant physiology · 2016Article
- Allerdictor: fast allergen prediction using text classification techniques.Bioinformatics (Oxford, England) · 2014Article
- PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection.BMC systems biology · 2013Article
- Evaluation and integration of existing methods for computational prediction of allergens.BMC bioinformatics · 2013Article
- Immunoinformatics: an integrated scenario.Immunology · 2010Review
- Scientific Opinion on the assessment of allergenicity of GM plants and microorganisms and derived food and feed.EFSA journal. European Food Safety Authority · 2010Article
- EVALLER: a web server for in silico assessment of potential protein allergenicity.Nucleic acids research · 2007Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on Filtered Length-adjusted Allergen Peptides (DFLAP). The DFLAP algorithm extracts variable length allergen sequence fragments and employs modern machine learning techniques in the form of a support vector machine. In particular, this new detector shows hitherto unmatched specificity when challenged to the Swiss-Prot repository without appreciable loss of sensitivity. DFLAP is also the first reported detector that successfully discriminates between allergens and non-allergens occurring in protein families known to hold both categories. Allergenicity assessment for specific protein sequences of interest using DFLAP is possible via ulfh@slv.se.
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