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KGG: a fully automated workflow for creating disease-specific knowledge graphs.

Reagon Karki1,2, Yojana Gadiya1,2,3, Andrea Zaliani1,2

  • 1Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Hamburg, 22525, Germany.

Bioinformatics (Oxford, England)
|June 28, 2025
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Summary
This summary is machine-generated.

This study introduces the Knowledge Graph Generator (KGG), an automated workflow for building life science knowledge graphs. KGG accelerates the creation of comprehensive biological and chemical entity networks, aiding disease research and drug discovery.

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational biology

Background:

  • Knowledge graphs (KGs) are crucial for systems biology, representing complex biological and pathophysiological data using standardized ontologies.
  • Manual KG construction is labor-intensive and time-consuming, while existing automated methods lack accuracy.
  • There is a need for efficient and reliable automated KG generation in the life sciences.

Purpose of the Study:

  • To develop an automated workflow for constructing life science knowledge graphs.
  • To represent chemotype and phenotype data for diseases and medical conditions.
  • To facilitate complex scientific queries and downstream analyses.

Main Methods:

  • Developed the Knowledge Graph Generator (KGG), an automated workflow.
  • Integrated data from curated databases (OpenTargets, Uniprot, ChEMBL, etc.).
  • Employed a schema resembling a clockwork mechanism for data integration.

Main Results:

  • Generated comprehensive KGs of disease-associated entities (proteins, pathways, chemicals, etc.).
  • Identified shared entities for comorbidity analysis and compared KGs with external sources.
  • Demonstrated use cases in Parkinson's Disease for target identification and drug repurposing.

Conclusions:

  • KGG provides an automated and efficient method for KG construction in life sciences.
  • The generated KGs support various applications, including comorbidity analysis and drug discovery.
  • The workflow enables exploration of drug-likeness and protein structure identification.