Publication: DINTO: Using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms
dc.affiliation.dpto | UC3M. Departamento de Informática | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT) | es |
dc.contributor.author | Herrero Zazo, María | |
dc.contributor.author | Segura-Bedmar, Isabel | |
dc.contributor.author | Hastings, Janna | |
dc.contributor.author | Martínez Fernández, Paloma | |
dc.contributor.funder | European Commission | es |
dc.date.accessioned | 2021-02-10T10:18:55Z | |
dc.date.available | 2021-02-10T10:18:55Z | |
dc.date.issued | 2015-08-01 | |
dc.description.abstract | The 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.sponsorship | This work was supported by the European Commission Seventh Framework Programme under the TrendMiner project [FP7-ICT287863]. | en |
dc.identifier.bibliographicCitation | J. Chem. Inf. Model. 2015, 55, 8, 1698–1707 | es |
dc.identifier.doi | https://doi.org/10.1021/acs.jcim.5b00119 | |
dc.identifier.issn | 1549-9596 | |
dc.identifier.publicationfirstpage | 1698 | |
dc.identifier.publicationissue | 8 | |
dc.identifier.publicationlastpage | 1707 | |
dc.identifier.publicationtitle | Journal of Chemical Information and Modeling | en |
dc.identifier.publicationvolume | 55 | |
dc.identifier.uri | https://hdl.handle.net/10016/31894 | |
dc.identifier.uxxi | AR/0000017239 | |
dc.language.iso | eng | es |
dc.publisher | ACS Publications | en |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/ICT287863 | es |
dc.rights | Copyright © 2015, American Chemical Society | es |
dc.rights.accessRights | open access | es |
dc.subject.eciencia | Informática | es |
dc.subject.other | Pharmacokinetics | en |
dc.subject.other | Pharmaceuticals | en |
dc.subject.other | Metabolism | en |
dc.subject.other | Peptides and proteins | en |
dc.subject.other | Mathematical methods | en |
dc.title | DINTO: Using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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