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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.

Yuan Zhang1,2, Xin Sui1, Feng Pan2

  • 1Department of Statistics, Florida State University, Tallahassee, FL 32306.

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|January 3, 2024
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Summary
This summary is machine-generated.

We built iKraph, a large biomedical knowledge graph from PubMed abstracts, achieving human-level accuracy for information extraction. This enables advanced automated knowledge discovery and drug repurposing, significantly improving research insights.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Scientific literature growth necessitates advanced data integration tools.
  • Knowledge graphs (KGs) are crucial for integrating heterogeneous biomedical data.
  • Automated knowledge discovery (AKD) faces challenges with unstructured text and accuracy.

Purpose of the Study:

  • To construct a large-scale, high-accuracy biomedical knowledge graph (iKraph) from PubMed abstracts.
  • To enhance KG comprehensiveness by integrating public databases and genomics data.
  • To develop and evaluate an AKD method for applications like drug repurposing.

Main Methods:

  • Utilized a top-ranked NLP pipeline for information extraction from PubMed abstracts.
  • Integrated data from 40 public databases and high-throughput genomics data.
  • Developed a probabilistic inference method for identifying indirect causal relations.

Main Results:

  • Constructed iKraph, a KG exceeding manual curation in content and matching human annotations.
  • Enabled rigorous AKD performance evaluation.
  • Identified 600-1400 candidate drugs monthly for COVID-19 repurposing, with validated discoveries.

Conclusions:

  • The iKraph KG and AKD methods provide a powerful resource for biomedical research.
  • The developed approach significantly advances automated knowledge discovery and drug repurposing.
  • A cloud platform is available for academic access to the KG and tools.