Provenance-Centered Dataset of Drug-Drug Interactions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9367)


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.


Drug-drug interactions Nanopublications Data mining 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Stanford University - Center for Biomedical Informatics ResearchStanfordUSA
  2. 2.Department of Humanities, Social and Political SciencesETH ZurichZürichSwitzerland
  3. 3.Department of Computer ScienceVU University AmsterdamAmsterdamNetherlands

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