Previously we created MELODI, a method and tool to derive overlapping enriched literature elements connecting two biomedical terms, e.g. an exposure and a disease, (Elsworth et al., 2018).
The main data involved were derived from SemMedDB (Kilicoglu et al., 2012), in particular a set of annotated ‘subject-predicate-object’ triples created from the titles and abstracts of almost 30 million biomedical articles.
Here, we present MELODI Presto. A quicker and more agile method to identify overlapping elements between any number of exposures and outcomes. The modifications made to the data, architecture and method are listed here https://github.com/MRCIEU/MELODI-Presto.