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PlantConnectome: A knowledge graph database encompassing >71,000 plant articles.

Shan Chun Lim1, Manoj Itharajula1, Mads Harder Møller2

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Summary
This summary is machine-generated.

Researchers used artificial intelligence (AI) to analyze plant biology literature, uncovering millions of gene and metabolite interactions. This AI-driven approach helps scientists track functional relationships in plant science.

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Area of Science:

  • Plant Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding gene-metabolite networks is crucial for plant development and environmental responses.
  • The increasing volume of scientific literature poses challenges for staying updated on functional genetics.
  • Text-mining and large language models (LLMs) offer potential solutions for literature analysis.

Purpose of the Study:

  • To leverage LLMs for text mining of plant biology abstracts to identify functional relationships.
  • To create a comprehensive database of biological entities and their interactions.
  • To aid plant scientists in navigating the vast scientific literature.

Main Methods:

  • Processed over 71,000 plant biology abstracts using LLM text-mining capabilities.
  • Identified and cataloged functional relationships between biological entities (genes, metabolites, etc.).
  • Developed the PlantConnectome database to store and present the extracted information.

Main Results:

  • Extracted nearly 5 million functional relationships between 2.4 million biological entities.
  • Achieved a high accuracy rate of over 85% in identifying these relationships.
  • Demonstrated the database's utility in analyzing gene regulatory networks, protein-protein interactions, and stress responses.

Conclusions:

  • AI-powered text mining can effectively process large volumes of scientific literature.
  • The PlantConnectome database provides valuable insights into plant functional networks.
  • This approach facilitates staying current with advances in plant biology research.