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Embeddings for drugs and side effects of interest from two widely used reference standards were then composed to generate embeddings of drug\/side-effect pairs, which were used as input for supervised machine learning. This methodology was developed and evaluated using cross-validation strategies and compared to contemporary approaches. To qualitatively assess generalization, models trained on the Observational Medical Outcomes Partnership (OMOP) drug\/side-effect reference set were evaluated against a list of\u2009\u22481100 drugs from an online database.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The employed method improved performance over previous approaches. Cross-validation results advance the state of the art (AUC 0.96; F1 0.90 and AUC 0.95; F1 0.84 across the two sets), outperforming methods utilizing literature and\/or spontaneous reporting system data. Examination of predictions for unseen drug\/side-effect pairs indicates the ability of these methods to generalize, with over tenfold label support enrichment in the top 100 predictions versus the bottom 100 predictions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion and Conclusion<\/jats:title><jats:p>Our methods can assist the pharmacovigilance process using information from the biomedical literature. Unsupervised pretraining generates a rich relationship-based representational foundation for machine learning techniques to classify drugs in the context of a putative side effect, given known examples.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocy077","type":"journal-article","created":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T19:19:59Z","timestamp":1528226399000},"page":"1339-1350","source":"Crossref","is-referenced-by-count":19,"title":["Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications"],"prefix":"10.1093","volume":"25","author":[{"given":"Justin","family":"Mower","sequence":"first","affiliation":[{"name":"Baylor College of Medicine, Quantitative and Computational Biosciences, Houston, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devika","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rice University, Houston, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trevor","family":"Cohen","sequence":"additional","affiliation":[{"name":"School of Biomedical Informatics, University of Texas Health Science Center Houston, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,7,11]]},"reference":[{"issue":"S1","key":"2020110612240892000_ocy077-B1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s40264-013-0097-8","article-title":"Defining a reference set to support methodological research in drug safety","volume":"36","author":"Ryan","year":"2013","journal-title":"Drug Saf"},{"issue":"6","key":"2020110612240892000_ocy077-B2","doi-asserted-by":"crossref","first-page":"374","DOI":"10.2165\/00002018-199717060-00004","article-title":"Causal or casual? 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