Large language models for bioinformatics

With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

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Publication Details

Journal
UNC Libraries
Published
2025-10-09
DOI
https://doi.org/10.17615/60dy-cj08
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
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article

Large language models for bioinformatics

Yingfeng Wang, Yingxiao Qian, Weidong Ruan, Wei Liu et al.
UNC Libraries
Genetics, Bioinformatics, and Biomedical Research
article

Large language models for bioinformatics

Yingfeng Wang, Yingxiao Qian, Weidong Ruan, Wei Liu, Yuan Dou, Tao Wang, Shuo Yu, Lin Zhao, Ge Yang, Zihao Wu, Dajiang Zhu, Yajun Yan, Yanjun Lyu, Zhengliang Liu, Hanqi Jiang, Xinyu Gong, Quanzheng Li, Shizhe Liang, Yusong Zou, Peng Shu, Lifang He, Yohannes Abate, Xianqiao Wang, Hongtu Zhu, Ping Ma, Qi Gan, Pengfei Zhang, Yue Wang, Xiang Li, Jin‐Jian Lu, Junhao Chen, Jianli Zhang, Yiwei Li, Tianming Liu, Peilong Wang, Yufang Liu, Jingyuan Chen, Ye Shen, Yi Pan, Jing Zhang, Wei Zhang, Yao Lu, Kenan Song, Ziyu Liu, Shang Gao, Andrea Sikora, Luyang Fang, Terry Ma, Chao Huang, Zhenyuan Yang, Jiazhang Cai, Gang Li, Rongjie Liu, Z. J. Chen
article en

Abstract

With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 75%
Genetics, Bioinformatics, and Biomedical Research
DOIOpen Access PDFOpenAlex
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