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The Innovation Nutrition > 2026 Vol. 1 > No. 1 > 100008
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Revisiting computational immunogenicity prediction for novel food ingredients

  • 1.

    Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China

  • 2.

    Division of Health Risk Factors Monitoring and Control, National Health Commission Specialty Laboratory of Food Safety Risk Assessment and Standard Development, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 201107, China

  • 3.

    School of Life Sciences, Shanghai University, Shanghai 200444, China

  • 4.

    Guangzhou National Laboratory, Guangzhou 510000, China

  • 5.

    The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou 510000, China

  • 6.

    Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China

  • 7.

    These authors contributed equally

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The Innovation Nutrition  1(1),  https://doi.org/10.59717/j.xinn-nutri.2026.100008  |  Cite this article
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    • REFERENCES
  • [1] FAO/WHO (2001). Evaluation of allergenicity of genetically modified foods: Report of a joint FAO/WHO expert consultation on allergenicity of foods derived from biotechnology, 22–25 January 2001. Rome/Geneva: FAO/WHO. https://iris.who.int/handle/10665/340572

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    [2] Sharma N., Patiyal S., Dhall A., et al. (2021). AlgPred 2.0: An improved method for predicting allergenic proteins and mapping of IgE epitopes. Brief Bioinform. 22:bbaa294. DOI:10.1093/bib/bbaa294

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    [3] Liu J., Negi S. S., Yang C., et al. (2025). AllergenAI: A deep learning model predicting allergenicity based on protein sequence. BMC Bioinform. 26:279. DOI:10.1186/s12859-025-06302-1

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    [4] Vojdani A., Gushgari L. R. and Vojdani E. (2020). Interaction between food antigens and the immune system: Association with autoimmune disorders. Autoimmun. Rev. 19:102459. DOI:10.1016/j.autrev.2020.102459

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    [5] Huang T. and Li Y. (2023). Current progress, challenges, and future perspectives of language models for protein representation and protein design. The Innovation 4:100446. DOI:10.1016/j.xinn.2023.100446

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  • Cite this article:

    Liu J., Cai H., Luo L., et al. (2026). Revisiting computational immunogenicity prediction for novel food ingredients. The Innovation Nutrition 1:100008. https://doi.org/10.59717/j.xinn-nutri.2026.100008
    Liu J., Cai H., Luo L., et al. (2026). Revisiting computational immunogenicity prediction for novel food ingredients. The Innovation Nutrition 1:100008. https://doi.org/10.59717/j.xinn-nutri.2026.100008

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