Advancing bioinformatics with language models: components, applications, and perspectives
13 affiliations
- 1Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, Houston, TX 77030 ,
- 2Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA
- 3Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center , No. 28 Xianning West Road, Xian City, Shaanxi Province, 710049, P.R
- 4School of Life Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- 5School of Computing and Artificial Intelligence, Southwest Jiaotong University , No. 999, Xi'an Road, Pidu District, Chengdu, Sichuan, 611756, P.R
- 6School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China
- 7The Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- 8West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- 9West China Biomedical Big Data Center, West China Hospital, Sichuan University , No. 17, Section 3, South Renmin Road, Chengdu, Sichuan, 610041, P.R
- 10McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- 11School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- 12McGovern Medical School, The University of Texas Health Science Center at Houston , 6431 Fannin St, Houston, Houston, TX 77030 ,
- 13School of Dentistry, The University of Texas Health Science Center at Houston , 7500 Cambridge St, Houston, TX 77030 ,
Abstract
Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field.
Related
- Engineering peroxygenases for pharmaceutical applications: recent advances and future perspectives.
- Pharmaceutical Applications of Nucleic Acid Aptamers: Recent Advances and Future Perspectives in Therapeutics, Biosensing, Bioanalysis, and Process Analysis
- AI and computational methods for reconstructing cell-state-specific gene regulatory networks from single-cell transcriptome and chromatin accessibility data in cancer: methods, applications, and perspectives
- Rare-Earth Orthoferrites: Synthesis–Structure–Property Relationships, Functional Applications, and Future Perspectives
- Development of bioinformatics competency framework to address labor market demands in Thailand
BibTeX
@article{liu2026advancing,
title = {{Advancing bioinformatics with language models: components, applications, and perspectives}},
author = {Jiajia Liu and Mengyuan Yang and Yankai Yu and Haixia Xu and Tiangang Wang and Kang Li and Xiaobo Zhou},
journal = {Briefings in Bioinformatics},
year = {2026},
month = jul,
doi = {10.1093/bib/bbag367},
url = {https://doi.org/10.1093/bib/bbag367},
note = {CC BY},
}
RIS
TY - JOUR
TI - Advancing bioinformatics with language models: components, applications, and perspectives
AU - Jiajia Liu
AU - Mengyuan Yang
AU - Yankai Yu
AU - Haixia Xu
AU - Tiangang Wang
AU - Kang Li
AU - Xiaobo Zhou
PY - 2026
DA - 2026/07/01
JO - Briefings in Bioinformatics
DO - 10.1093/bib/bbag367
UR - https://doi.org/10.1093/bib/bbag367
AB - Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field.
SN - 1467-5463
SN - 1477-4054
ER -