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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge Graphs and Their Applications in Drug Discovery.

Tim James1, Holger Hennig2

  • 1Evotec (UK) Ltd., Abingdon, Oxfordshire, UK. tim.james@evotec.com.

Methods in Molecular Biology (Clifton, N.J.)
|September 13, 2023
PubMed
Summary
This summary is machine-generated.

Knowledge graphs organize biomedical data, enabling machine learning for drug discovery insights. They provide context for explainable AI predictions, advancing pharmaceutical research.

Keywords:
Artificial intelligenceDrug discoveryExplainable AIGraph convolutionGraph embeddingKnowledge graphMachine learningNatural language processingTransformers

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Drug Discovery

Background:

  • Knowledge graphs represent complex information as entities and relationships.
  • Biomedical data integration presents challenges in accessibility and contextualization.
  • Explainable predictions are crucial for contemporary artificial intelligence applications.

Purpose of the Study:

  • To explore the construction of biomedical knowledge graphs.
  • To examine machine learning applications for drug discovery insights.
  • To identify future directions in biomedical knowledge graph development.

Main Methods:

  • Reviewing factors for constructing biomedical knowledge graphs.
  • Analyzing recent advances in mining knowledge graph systems.
  • Identifying potential future research areas.

Main Results:

  • Knowledge graphs democratize access to biomedical data.
  • They contextualize and visualize data for better understanding.
  • Machine learning on knowledge graphs generates novel drug discovery insights.

Conclusions:

  • Biomedical knowledge graphs are valuable tools for drug discovery.
  • They facilitate explainable AI through contextualized data.
  • Further development is needed to maximize their potential.