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- W4380853828 abstract "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." @default.
- W4380853828 created "2023-06-16" @default.
- W4380853828 creator A5008554513 @default.
- W4380853828 creator A5037699975 @default.
- W4380853828 creator A5057875428 @default.
- W4380853828 creator A5085449381 @default.
- W4380853828 date "2023-06-15" @default.
- W4380853828 modified "2023-10-14" @default.
- W4380853828 title "Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers" @default.
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- W4380853828 doi "https://doi.org/10.1038/s41467-023-39301-y" @default.
- W4380853828 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37322032" @default.
- W4380853828 hasPublicationYear "2023" @default.
- W4380853828 type Work @default.
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