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.
Chapter PDF
Similar content being viewed by others
References
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)
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
Bushardt, R.L., Massey, E.B., Simpson, T.W., Ariail, J.C., Simpson, K.N.: Polypharmacy: Misleading, but manageable. Clinical Interventions in Aging. 3(2), 383–389 (2008). 18686760[pmid] Clin Interv Aging
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)
Dumontier, M., Baker, C.J., Baran, J., Callahan, A., Chepelev, L.L., Cruz-Toledo, J., Nicholas, R., Rio, D., Duck, G., Furlong, L.I., et al.: The semanticscience integrated ontology (sio) for biomedical research and knowledge discovery. J. Biomedical Semantics 5, 14 (2014)
Gottlieb, A., Stein, G.Y., Oron, Y., Ruppin, E., Sharan, R.: Indi: a computational framework for inferring drug interactions and their associated recommendations. Molecular Systems Biology 8, 592–592 (2012). 22806140[pmid] Mol. Syst. Biol
Groth, P., Gibson, A., Velterop, J.: The anatomy of a nano-publication. Information Services and Use 30(1), 51–56 (2010)
Haerian, K., Varn, D., Vaidya, S., Ena, L., Chase, H.S., Friedman, C.: Detection of pharmacovigilance-related adverse events using electronic health records and automated methods. Clinical pharmacology and therapeutics 92(2), 228–234 (2012)
Iyer, S.V., Harpaz, R., LePendu, P., Bauer-Mehren, A., Shah, N.H.: Mining clinical text for signals of adverse drug-drug interactions. J. Am. Med. Inform. Assoc. 21(2), 353–362 (2014)
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)
Kuhn, T., Dumontier, M.: Trusty URIs: verifiable, immutable, and permanent digital artifacts for linked data. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 395–410. Springer, Heidelberg (2014)
Kuhn, T., Dumontier, M.: Making digital artifacts on the web verifiable and reliable. IEEE Transactions on Knowledge and Data Engineering (2015)
Lazarou, J., Pomeranz, B.H., Corey, P.N.: Incidence of adverse drug reactions in hospitalized patients: A meta-analysis of prospective studies. JAMA 279(15), 1200–1205 (1998). doi:10.1001/jama.279.15.1200
Lebo, T., et al.: PROV-O: The PROV ontology. Recommendation, W3C (2013)
Mons, B., van Haagen, H., Chichester, C., den Dunnen, J.T., van Ommen, G., van Mulligen, E., Singh, B., Hooft, R., Roos, M., Hammond, J., et al.: The value of data. Nature genetics 43(4), 281–283 (2011)
Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Science Translational Medicine 4(125), 125ra31 (2012). doi:10.1126/scitranslmed.3003377
Vilar, S., Uriarte, E., Santana, L., Lorberbaum, T., Hripcsak, G., Friedman, C., Tatonetti, N.P.: Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat. Protocols 9(9), 2147–2163 (2014)
Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M.: Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Research 36, D901–D906 (2008). 18048412[pmid] Nucleic Acids Res
Zhang, L., Zhang, Y., Zhao, P., Huang, S.-M.: Predicting drugdrug interactions: An fda perspective. The AAPS Journal 11(2), 300–306 (2009)
Linked Drug-Drug Interactions (LIDDI) dataset. Nanopublication index, July 17, 2015. http://np.inn.ac/RA7SuQ0e661LJdKpt5EOS2DKykf1ht9LFmNaZtFSDMrXg
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Banda, J.M., Kuhn, T., Shah, N.H., Dumontier, M. (2015). Provenance-Centered Dataset of Drug-Drug Interactions. In: Arenas, M., et al. The Semantic Web - ISWC 2015. ISWC 2015. Lecture Notes in Computer Science(), vol 9367. Springer, Cham. https://doi.org/10.1007/978-3-319-25010-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-25010-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25009-0
Online ISBN: 978-3-319-25010-6
eBook Packages: Computer ScienceComputer Science (R0)