Recapitulation and Retrospective Prediction of Biomedical Associations Using Temporally-enabled Word Embeddings

Abstract

The recent explosion of biomedical knowledge presents both a major opportunity and challenge for scientists tackling complex problems in healthcare. Here we present an approach for synthesizing biomedical knowledge based on a combination of word-embeddings and select cooccurrences. We evaluated our ability to recapitulate and retrospectively predict disease-gene associations from the Online Mendelian Inheritance in Man (OMIM) resource. Our metrics achieved an area under the curve (AUC) value of 0.981 at the recapitulation task for 2,400 disease-gene associations. At the most stringent cutoff, our metrics predicted 13.89% of these associations before their first cooccurrence in the literature, with a median time of 4 years between prediction and first cooccurrence. Finally, our literature metrics can be combined with human genetics data to retrospectively predict disease-gene associations, IL-6 and Giant Cell Arteritis provided as an example. We believe this framework can provide robust biomedical hypotheses at a much faster pace than current standard practices.