Provenance-Centered Dataset of Drug-Drug Interactions
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.
KeywordsDrug-drug interactions Nanopublications Data mining
Unable to display preview. Download preview PDF.
- 1.Avillach, P., Dufour, J.-C., Diallo, G., Salvo, F., Joubert, M., Thiessard, F., Mougin, F., Trifirò, G., Fourrier-Réglat, A., Pariente, A., Fieschi, M.: Design and validation of an automated method to detect known adverse drug reactions in medline: a contribution from the eu–adr project. Journal of the American Medical Informatics Association 20(3), 446–452 (2013)CrossRefGoogle Scholar
- 2.Banda, J.M., Kuhn, T., Shah, N.H., Dumontier, M.: Liddi: Provenance-centered dataset of drug-drug interactions. figshare July 17, 2015. http://dx.doi.org/10.6084/m9.figshare.1486478
- 4.Callahan, A., Cruz-Toledo, J., Ansell, P., Dumontier, M.: Bio2RDF release 2: improved coverage, interoperability and provenance of life science linked data. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 200–212. Springer, Heidelberg (2013) CrossRefGoogle Scholar
- 7.Groth, P., Gibson, A., Velterop, J.: The anatomy of a nano-publication. Information Services and Use 30(1), 51–56 (2010)Google Scholar
- 10.T. Kuhn, C. Chichester, M. Krauthammer, and M. Dumontier. Publishing without publishers: a decentralized approach to dissemination, retrieval, and archiving of data. In: Proceedings of ISWC 2015. Lecture Notes in Computer Science. Springer (2015)Google Scholar
- 12.Kuhn, T., Dumontier, M.: Making digital artifacts on the web verifiable and reliable. IEEE Transactions on Knowledge and Data Engineering (2015)Google Scholar
- 14.Lebo, T., et al.: PROV-O: The PROV ontology. Recommendation, W3C (2013)Google Scholar
- 20.Linked Drug-Drug Interactions (LIDDI) dataset. Nanopublication index, July 17, 2015. http://np.inn.ac/RA7SuQ0e661LJdKpt5EOS2DKykf1ht9LFmNaZtFSDMrXg