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Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers

Author

Listed:
  • Dongmin Bang

    (Seoul National University
    AIGENDRUG Co., Ltd.)

  • Sangsoo Lim

    (Dongguk University)

  • Sangseon Lee

    (Seoul National University)

  • Sun Kim

    (Seoul National University
    AIGENDRUG Co., Ltd.
    Seoul National University
    Seoul National University)

Abstract

Computational drug repurposing aims to identify new indications for existing drugs by utilizing high-throughput data, often in the form of biomedical knowledge graphs. However, learning on biomedical knowledge graphs can be challenging due to the dominance of genes and a small number of drug and disease entities, resulting in less effective representations. To overcome this challenge, we propose a “semantic multi-layer guilt-by-association" approach that leverages the principle of guilt-by-association - “similar genes share similar functions", at the drug-gene-disease level. Using this approach, our model DREAMwalk: Drug Repurposing through Exploring Associations using Multi-layer random walk uses our semantic information-guided random walk to generate drug and disease-populated node sequences, allowing for effective mapping of both drugs and diseases in a unified embedding space. Compared to state-of-the-art link prediction models, our approach improves drug-disease association prediction accuracy by up to 16.8%. Moreover, exploration of the embedding space reveals a well-aligned harmony between biological and semantic contexts. We demonstrate the effectiveness of our approach through repurposing case studies for breast carcinoma and Alzheimer’s disease, highlighting the potential of multi-layer guilt-by-association perspective for drug repurposing on biomedical knowledge graphs.

Suggested Citation

  • Dongmin Bang & Sangsoo Lim & Sangseon Lee & Sun Kim, 2023. "Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39301-y
    DOI: 10.1038/s41467-023-39301-y
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    References listed on IDEAS

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    1. Camilo Ruiz & Marinka Zitnik & Jure Leskovec, 2021. "Identification of disease treatment mechanisms through the multiscale interactome," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Stephen Oliver, 2000. "Guilt-by-association goes global," Nature, Nature, vol. 403(6770), pages 601-602, February.
    3. Catia Pesquita & Daniel Faria & André O Falcão & Phillip Lord & Francisco M Couto, 2009. "Semantic Similarity in Biomedical Ontologies," PLOS Computational Biology, Public Library of Science, vol. 5(7), pages 1-12, July.
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