Large Language Models in Bioinformatics: A Survey

Zhenyu Wang; Zikang Wang; Jiyue Jiang; Pengan Chen; Xiangyu Shi (石响宇); Yu Li (李豫, 李宇)

doi:10.18653/v1/2025.findings-acl.184

Large Language Models in Bioinformatics: A Survey

Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen, Xiangyu Shi, Yu Li

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Abstract

Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine.

Anthology ID:: 2025.findings-acl.184
Volume:: Findings of the Association for Computational Linguistics: ACL 2025
Month:: July
Year:: 2025
Address:: Vienna, Austria
Editors:: Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 3602–3615
Language:
URL:: https://aclanthology.org/2025.findings-acl.184/
DOI:: 10.18653/v1/2025.findings-acl.184
Bibkey:
Cite (ACL):: Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen, Xiangyu Shi, and Yu Li. 2025. Large Language Models in Bioinformatics: A Survey. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3602–3615, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: Large Language Models in Bioinformatics: A Survey (Wang et al., Findings 2025)
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PDF:: https://aclanthology.org/2025.findings-acl.184.pdf

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@inproceedings{wang-etal-2025-large-language,
    title = "Large Language Models in Bioinformatics: A Survey",
    author = "Wang, Zhenyu  and
      Wang, Zikang  and
      Jiang, Jiyue  and
      Chen, Pengan  and
      Shi, Xiangyu  and
      Li, Yu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.184/",
    doi = "10.18653/v1/2025.findings-acl.184",
    pages = "3602--3615",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine."
}

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%0 Conference Proceedings
%T Large Language Models in Bioinformatics: A Survey
%A Wang, Zhenyu
%A Wang, Zikang
%A Jiang, Jiyue
%A Chen, Pengan
%A Shi, Xiangyu
%A Li, Yu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-large-language
%X Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine.
%R 10.18653/v1/2025.findings-acl.184
%U https://aclanthology.org/2025.findings-acl.184/
%U https://doi.org/10.18653/v1/2025.findings-acl.184
%P 3602-3615

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Markdown (Informal)

[Large Language Models in Bioinformatics: A Survey](https://aclanthology.org/2025.findings-acl.184/) (Wang et al., Findings 2025)

Large Language Models in Bioinformatics: A Survey (Wang et al., Findings 2025)

ACL

Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen, Xiangyu Shi, and Yu Li. 2025. Large Language Models in Bioinformatics: A Survey. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3602–3615, Vienna, Austria. Association for Computational Linguistics.