Applications of large‐scale artificial intelligence models in bioinformatics

Large-scale artificial intelligence (AI) models can mine potential patterns from massive amounts of data and provide more accurate analyses. This capability has enabled its gradual application in various areas of bioinformatics. However, few reviews have comprehensively summarized the applications of different types of large-scale AI models in key areas of bioinformatics. Therefore, we first introduce the concept of large-scale AI models and classify them into three types. Second, we summarize the key methods, applications, and resources of these three types of bioinformatics models. Finally, we discuss challenges and directions for future research. This review provides researchers with a comprehensive perspective to better understand the applications of large-scale AI models in bioinformatics.

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Publication Details

Journal
Quantitative Biology
Published
2025-12-22
DOI
https://doi.org/10.1002/qub2.70026
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00

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article

Applications of large‐scale artificial intelligence models in bioinformatics

Z. S. You, Li Zhang, Ming Xiao, Q Shang et al.
Quantitative Biology
Machine Learning in Bioinformatics
article

Applications of large‐scale artificial intelligence models in bioinformatics

Z. S. You, Li Zhang, Ming Xiao, Q Shang, Ziyang Dong, Mingjing Li
article en

Abstract

Large-scale artificial intelligence (AI) models can mine potential patterns from massive amounts of data and provide more accurate analyses. This capability has enabled its gradual application in various areas of bioinformatics. However, few reviews have comprehensively summarized the applications of different types of large-scale AI models in key areas of bioinformatics. Therefore, we first introduce the concept of large-scale AI models and classify them into three types. Second, we summarize the key methods, applications, and resources of these three types of bioinformatics models. Finally, we discuss challenges and directions for future research. This review provides researchers with a comprehensive perspective to better understand the applications of large-scale AI models in bioinformatics.

Quantitative BiologyVol. 14(2)
Sichuan University (CN)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 37%
Machine Learning in Bioinformatics
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