Foundation Models in Bioinformatics

Authors

Fei Guo, Central South University
Renchu Guan, Jilin University
Yaohang Li, Old Dominion UniversityFollow
Qi Liu, Tongji University
Xiaowo Wang, Tsinghua University
Can Yang, The Hong Kong University of Science and Technology
Jianxin Wang, Central South University

Document Type

Article

Publication Date

2025

DOI

10.1093/nsr/nwaf028

Publication Title

National Science Review

Volume

12

Issue

4

Pages

nwaf028 (1-19)

Abstract

With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities. A new era in computational biology has been ushered in by the application of FMs, focusing on both general and specific biological issues. In this review, we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks, including genomics, transcriptomics, proteomics, drug discovery and single-cell analysis. Our aim is to assist scientists in selecting appropriate FMs in bioinformatics, according to four model types: language FMs, vision FMs, graph FMs and multimodal FMs. In addition to understanding molecular landscapes, AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

Rights

© The Authors 2025.

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025). Foundation models in bioinformatics. National Science Review, 12(4), 1-19, Article nwaf028. https://doi.org/10.1093/nsr/nwaf028

Repository Citation

Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025). Foundation models in bioinformatics. National Science Review, 12(4), 1-19, Article nwaf028. https://doi.org/10.1093/nsr/nwaf028

ORCID

0000-0003-0178-1876 (Li)