Drug Similarity and Link Prediction Using Graph Embeddings on Medical Knowledge Graphs
This addresses drug recommendation for medical experts to reduce side effects, but appears incremental as it builds on existing graph embedding and link prediction techniques.
The paper tackles drug similarity and link prediction using graph embeddings on medical knowledge graphs, proposing a novel node similarity measure that combines embeddings and link prediction scores to recommend alternative drugs and avoid side effects, with claims of being less costly, time-consuming, and more scalable than traditional biomedical methods.
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 compared to traditional biomedical methods due to the dependency on costly medical equipment and experts of the latter ones.