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Inferring knowledge from clinical trials using knowledge graph embedding isan emerging area. However, customizing graph embeddings for different use casesremains a significant challenge. We propose custom2vec, an algorithmicframework to customize graph embeddings by incorporating user preferences intraining the embeddings. It captures user preferences by adding custom nodesand links derived from manually vetted results of a separate informationretrieval method. We propose a joint learning objective to preserve theoriginal network structure while incorporating the user's custom annotations.We hypothesize that the custom training improves user-expected predictions, forexample, in link prediction tasks. We demonstrate the effectiveness ofcustom2vec for clinical trials related to non-small cell lung cancer (NSCLC)with two customization scenarios: recommending immuno-oncology trialsevaluating PD-1 inhibitors and exploring similar trials that compare newtherapies with a standard of care. The results show that custom2vec trainingachieves better performance than the conventional training methods. Ourapproach is a novel way to customize knowledge graph embeddings and enable moreaccurate recommendations and predictions.

Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation