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Updated: Jun 18, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth
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Advancing plant biology through deep learning-powered natural language processing.

Shuang Peng1, Loïc Rajjou2

  • 1Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000, Versailles, France.

Plant Cell Reports
|August 5, 2024
PubMed
Summary
This summary is machine-generated.

Large Language Models (LLMs) and Protein Language Models (PLMs) offer powerful tools for analyzing plant DNA and protein sequences. These deep learning methods promise breakthroughs in plant biology, agriculture, and sustainable food systems.

Keywords:
BiologyDNADeep learningLarge language modelsPlant sciencesProteins

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Area of Science:

  • Plant Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Deep learning, particularly Large Language Models (LLMs), is emerging as a transformative technology in biological sciences.
  • Plant biology research can benefit from advanced computational methods for analyzing complex biological data.

Purpose of the Study:

  • To explore the potential of LLMs and Protein Language Models (PLMs) in advancing plant science research.
  • To highlight the application of these models in understanding plant cell systems and genetic improvements.

Main Methods:

  • Utilizing LLM frameworks for in-depth analysis of nucleic acid and protein sequences.
  • Applying PLMs for pattern recognition and structure-function relationship identification in biological data.
  • Integrating deep learning approaches to analyze multi-scale information within DNA and protein sequences.

Main Results:

  • LLMs and PLMs enable detailed analysis of intricate patterns and relationships in plant genetic data.
  • These models facilitate understanding of multi-scale information within DNA and protein sequences.
  • The application of these methods supports advancements in agricultural genetic improvements.

Conclusions:

  • Deep learning, especially LLMs and PLMs, offers significant promise for novel knowledge generation in plant biology.
  • These computational tools can drive breakthroughs in basic research across various plant traits.
  • Strategic implementation of LLMs will accelerate progress in plant sciences, production, and sustainable agro-food systems.