Provenance-centered dataset of drug-drug interactions

Juan M. Banda*, Tobias Kuhn, N.H. Shah, M. Dumontier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (medline), electronic health records, public databases (drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present linked drug-drug interactions (liddi), a public nanopublication-based rdf dataset with trusty uris that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.
Original languageEnglish
Title of host publicationThe Semantic Web - ISWC 2015
Subtitle of host publication14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part II
PublisherSpringer, Cham
Number of pages8
ISBN (Electronic)9783319250106
ISBN (Print)9783319250090
Publication statusPublished - 2015
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science
SeriesInformation Systems and Applications, incl. Internet/Web, and HCI

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