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Foundation models in molecular biology.

Yunda Si1, Jiawei Zou2, Yicheng Gao3,4

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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.

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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.