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DINTO: Using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorHerrero Zazo, María
dc.contributor.authorSegura-Bedmar, Isabel
dc.contributor.authorHastings, Janna
dc.contributor.authorMartínez Fernández, Paloma
dc.contributor.funderEuropean Commissiones
dc.date.accessioned2021-02-10T10:18:55Z
dc.date.available2021-02-10T10:18:55Z
dc.date.issued2015-08-01
dc.description.abstractThe early detection of drug drug interactions (DDIs) is limited by the diffuse spread of DDI information in heterogeneous sources. Computational methods promise to play a key role in the identification and explanation of DDIs on a large scale. However, such methods rely on the availability of computable representations describing the relevant domain knowledge. Current modeling efforts have focused on partial and shallow representations of the DDI domain, failing to adequately support computational inference and discovery applications. In this paper, we describe a comprehensive ontology for DDI knowledge (DINTO), which is the first formal representation of different types of DDIs and their mechanisms and its application in the prediction of DDIs. This project has been developed using currently available semantic web technologies, standards, and tools, and we have demonstrated that the combination of drug-related facts in DINTO and Semantic Web Rule Language (SWRL) rules can be used to infer DDIs and their different mechanisms on a large scale.<en
dc.description.sponsorshipThis work was supported by the European Commission Seventh Framework Programme under the TrendMiner project [FP7-ICT287863].en
dc.identifier.bibliographicCitationJ. Chem. Inf. Model. 2015, 55, 8, 1698–1707es
dc.identifier.doihttps://doi.org/10.1021/acs.jcim.5b00119
dc.identifier.issn1549-9596
dc.identifier.publicationfirstpage1698
dc.identifier.publicationissue8
dc.identifier.publicationlastpage1707
dc.identifier.publicationtitleJournal of Chemical Information and Modelingen
dc.identifier.publicationvolume55
dc.identifier.urihttps://hdl.handle.net/10016/31894
dc.identifier.uxxiAR/0000017239
dc.language.isoenges
dc.publisherACS Publicationsen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/ICT287863es
dc.rightsCopyright © 2015, American Chemical Societyes
dc.rights.accessRightsopen accesses
dc.subject.ecienciaInformáticaes
dc.subject.otherPharmacokineticsen
dc.subject.otherPharmaceuticalsen
dc.subject.otherMetabolismen
dc.subject.otherPeptides and proteinsen
dc.subject.otherMathematical methodsen
dc.titleDINTO: Using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanismsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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