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
Quantitative Biology
Published
2025-09-28
DOI
https://doi.org/10.1002/qub2.70014
Citations
5
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
4.03
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article

Large language models for bioinformatics

Yingfeng Wang, Yingxiao Qian, Weidong Ruan, Yuan Dou et al.
5 citations
Quantitative Biology
Machine Learning in Bioinformatics
4.03
article

Large language models for bioinformatics

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

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.

Quantitative BiologyVol. 14(1)
University of North Carolina at Chapel Hill (US), University of North Carolina Health Care (US), Harvard University (US), University of Georgia (US), University of Tennessee at Chattanooga (US), Lehigh University (US), University of Colorado Boulder (US), The University of Texas at Arlington (US), University of Colorado System (US), WinnMed (US), Augusta University Health (US), Massachusetts General Hospital (US), Mayo Clinic in Florida (US), Indiana University – Purdue University Indianapolis (US), Carnegie Mellon University (US)
Openalex Percentile: Top 6%
Machine Learning in Bioinformatics
4.03
DOIOpen Access PDFOpenAlex
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