Drug Similarity and Link Prediction Using Graph Embeddings on Medical Knowledge Graphs

• URL: http://arxiv.org/abs/2110.13047v1
• Date: Fri, 22 Oct 2021 06:22:36 GMT
• Title: Drug Similarity and Link Prediction Using Graph Embeddings on Medical Knowledge Graphs
• Authors: Prakhar Gurawa and Matthias Nickles
• Abstract summary: The paper utilizes the graph embeddings generated for entities of a large biomedical database to perform link prediction. A novel node similarity measure is proposed that utilizes the graph embeddings and link prediction scores to find similarity scores among various drugs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper utilizes the graph embeddings generated for entities of a large biomedical database to perform link prediction to capture various new relationships among different entities. A novel node similarity measure is proposed that utilizes the graph embeddings and link prediction scores to find similarity scores among various drugs which can be used by the medical experts to recommend alternative drugs to avoid side effects from original one. Utilizing machine learning on knowledge graph for drug similarity and recommendation will be less costly and less time consuming with higher scalability as compare to traditional biomedical methods due to the dependency on costly medical equipment and experts by the later ones.

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