  Inferring knowledge from clinical trials using knowledge graph embedding is
an emerging area. However, customizing graph embeddings for different use cases
remains a significant challenge. We propose custom2vec, an algorithmic
framework to customize graph embeddings by incorporating user preferences in
training the embeddings. It captures user preferences by adding custom nodes
and links derived from manually vetted results of a separate information
retrieval method. We propose a joint learning objective to preserve the
original network structure while incorporating the user's custom annotations.
We hypothesize that the custom training improves user-expected predictions, for
example, in link prediction tasks. We demonstrate the effectiveness of
custom2vec for clinical trials related to non-small cell lung cancer (NSCLC)
with two customization scenarios: recommending immuno-oncology trials
evaluating PD-1 inhibitors and exploring similar trials that compare new
therapies with a standard of care. The results show that custom2vec training
achieves better performance than the conventional training methods. Our
approach is a novel way to customize knowledge graph embeddings and enable more
accurate recommendations and predictions.
