Large Language Models in Bioinformatics: A Survey
Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen, Xiangyu Shi, Yu Li
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.
BibTeX
@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"
}