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Foundation models for bioinformatics.

Ziyu Chen1,2, Lin Wei1,2, Ge Gao1,2

  • 1State Key Laboratory of Protein and Plant Gene Research School of Life Sciences Biomedical Pioneering Innovative Center (BIOPIC) & Beijing Advanced Innovation Center for Genomics (ICG) Center for Bioinformatics (CBI) Peking University Beijing China.

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Summary
This summary is machine-generated.

Foundation models, like ChatGPT, are transforming bioinformatics. This perspective explores their direct use, tailored bioinformatics models, and future challenges for large language models in the field.

Keywords:
ChatGPTfoundation modelslarge language modelstransformer

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Area of Science:

  • Bioinformatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • Transformer-based foundation models, exemplified by ChatGPT, have significantly impacted various scientific domains.
  • The integration of large language models (LLMs) into bioinformatics presents both opportunities and challenges.

Purpose of the Study:

  • To review the direct applications of textual foundation models in bioinformatics.
  • To discuss the development and potential of bioinformatics-tailored foundation models for diverse data types.
  • To outline future directions and inherent challenges in the field of bioinformatics foundation models.

Main Methods:

  • Review of current literature on foundation models in bioinformatics.
  • Analysis of canonical large language models for bioinformatics tasks.
  • Exploration of transformer-based models adapted for biological sequence and non-sequence data.

Main Results:

  • Direct application of LLMs in bioinformatics requires strategies to maximize utility and mitigate limitations.
  • Specialized foundation models are emerging for biological sequence and non-sequence data analysis.
  • Significant potential exists for advanced bioinformatics foundation models, alongside notable challenges.

Conclusions:

  • Foundation models offer transformative potential for bioinformatics research and applications.
  • Tailored models and careful application of general LLMs are crucial for advancing the field.
  • Addressing inherent challenges will be key to unlocking the full capabilities of bioinformatics foundation models.