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.
View on arXiv@article{wang2025_2503.04490,
title={ Large Language Models in Bioinformatics: A Survey },
author={ Zhenyu Wang and Zikang Wang and Jiyue Jiang and Pengan Chen and Xiangyu Shi and Yu Li },
journal={arXiv preprint arXiv:2503.04490},
year={ 2025 }
}