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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Constructing knowledge graphs and their biomedical applications.

David N Nicholson1, Casey S Greene2

  • 1Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, United States.

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|July 9, 2020
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Summary
This summary is machine-generated.

Machine learning is revolutionizing biomedical knowledge graph construction and application. These advanced methods automate graph creation and enable powerful predictions in genomics, pharmaceuticals, and clinical research.

Keywords:
Lterature reviewMachine learningNatural language processingNetwork embeddingsText miningknowledge graphs

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Area of Science:

  • Biomedical informatics
  • Machine learning
  • Data science

Background:

  • Biomedical knowledge graphs integrate complex biological data.
  • Traditionally, manual curation by experts built these graphs.
  • Automated systems are increasingly used for graph construction.

Purpose of the Study:

  • To review the construction and application of biomedical knowledge graphs.
  • To highlight the impact of machine learning on these processes.
  • To explore future directions in the field.

Main Methods:

  • Utilizing machine learning for automated knowledge graph construction.
  • Employing representational learning to create low-dimensional graph embeddings.
  • Applying these representations for predictive tasks in various biomedical domains.

Main Results:

  • Machine learning enhances the automation and robustness of knowledge graph creation.
  • Low-dimensional representations preserve graph structure for diverse applications.
  • Successful applications demonstrated in genomic, pharmaceutical, and clinical research.

Conclusions:

  • Machine learning advances are transforming biomedical knowledge graph applications.
  • Unified representational learning techniques offer significant potential.
  • Future work in knowledge graphs promises novel biomedical discoveries.