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Foundation models in molecular biology
Yunda Si1, Jiawei Zou2, Yicheng Gao3,4
1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China.
Biophysics Reports
|July 19, 2024
Summary
Foundation models, or large language models, are advancing molecular biology by uncovering molecular correlations. These models are being developed using diverse biological data, driving significant progress in the field.
Area of Science:
- Molecular Biology
- Computational Biology
- Bioinformatics
Background:
- Determining molecular correlations is crucial in molecular biology.
- Large language models (LLMs) excel at capturing complex data correlations.
- LLMs, also known as foundation models, show promise for biological applications.
Purpose of the Study:
- To review foundation models developed for molecular biology.
- To highlight advancements driven by foundation models in the field.
- To discuss future research directions for these models.
Main Methods:
- Summarizing foundation models based on RNA sequence data.
- Reviewing foundation models for DNA sequence data.
- Analyzing foundation models for protein sequence data.
- Examining foundation models for single-cell and spatial transcriptome data.
Main Results:
- Foundation models are emerging across various molecular data types.
- These models leverage large biological datasets for correlation discovery.
- Recent developments have significantly advanced molecular biology research.
Conclusions:
- Foundation models are transformative tools in molecular biology.
- Their application spans diverse data types from sequences to spatial transcriptomes.
- Continued development holds potential for future breakthroughs.
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