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dc.contributor.authorCaniza, Horacio Jose 
dc.contributor.authorGaleano Galeano, Diego Ariel 
dc.contributor.authorPaccanaro, Alberto 
dc.date.accessioned2022-04-06T23:05:55Z
dc.date.available2022-04-06T23:05:55Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/20.500.14066/2840
dc.description.abstractThe current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the biomedical literature. We show that using MeSH ontology terms can accurately describe the drugs and that appropriate use of the MeSH ontological structure can determine pairwise drug similarity.es
dc.description.sponsorshipCONACYT – Consejo Nacional de Ciencia y Tecnologíaes
dc.language.isoenges
dc.subject.classification6 Producción y tecnología industriales
dc.subject.otherMESH TERMSes
dc.subject.otherDRUG TARGETSes
dc.subject.otherDRUG DESCRIPTORSes
dc.subject.otherDRUGBANKes
dc.subject.otherBIOMEDICAL LITERATUREes
dc.subject.otherBIOMEDICINAes
dc.subject.otherFARMACOLOGIAes
dc.subject.otherINDUSTRIA FARMACEUTICAes
dc.titleMining the biomedical literature to predict shared drug targets in DrugBankes
dc.typeresearch articlees
dc.identifier.doi10.1109/CLEI.2017.8226376es
dc.description.fundingtextPROCIENCIAes
dc.relation.projectCONACYT14-INV-088es
dc.rights.accessRightsopen accesses
dc.rights.copyright© 2017 IEEE European Uniones


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