Provenance-Centered Dataset of Drug-Drug Interactions View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Juan M. Banda , Tobias Kuhn , Nigam H. Shah , Michel Dumontier

ABSTRACT

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. More... »

PAGES

293-300

Book

TITLE

The Semantic Web - ISWC 2015

ISBN

978-3-319-25009-0
978-3-319-25010-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-25010-6_18

DOI

http://dx.doi.org/10.1007/978-3-319-25010-6_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001714658


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