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textToKnowledgeGraph: Generation of Molecular Interaction Knowledge Graphs Using Large Language Models for

Favour James1, Christopher Churas2, Dexter Pratt2

  • 1Department of Electronic and Electrical Engineering, Obafemi Awolowo University.

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Summary
This summary is machine-generated.

This study introduces textToKnowledgeGraph, an AI tool leveraging Large Language Models (LLMs) to automatically extract biological relationships from scientific literature into Biological Expression Language (BEL) format.

Keywords:
Biological NetworksKnowledge GraphsLarge Language ModelsRetrieval Augmented Generation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Knowledge graphs (KGs) are vital for biological data analysis but manual construction from literature is resource-intensive.
  • Existing text-mining tools struggle with contextual understanding and inferring complex biological relationships.
  • Large Language Models (LLMs) offer enhanced contextual knowledge for more accurate information extraction.

Purpose of the Study:

  • To develop an automated method for extracting biological interactions from unstructured text.
  • To represent extracted relationships in the structured Biological Expression Language (BEL) format.
  • To overcome limitations of traditional text-mining approaches in capturing complex biological context.

Main Methods:

  • Utilized Large Language Models (LLMs) for advanced text understanding and relationship extraction.
  • Developed the open-source Python package textToKnowledgeGraph for automated BEL extraction.
  • Integrated an interactive application within Cytoscape Web for simplified extraction and exploration.

Main Results:

  • Successfully extracted biological interactions from scientific articles into BEL format.
  • Created an open-source tool and an interactive application for knowledge graph construction.
  • Generated a reviewed dataset to facilitate future advancements in automated biological knowledge extraction.

Conclusions:

  • LLM-powered textToKnowledgeGraph effectively automates the extraction of biological relationships.
  • The developed tool and dataset lower barriers to creating structured biological knowledge graphs.
  • This approach enhances the accessibility and utility of biological literature for computational analysis.