| dc.contributor.author | Caniza Vierci, Horacio José | |
| dc.contributor.author | Galeano Galeano, Diego Ariel | |
| dc.contributor.author | Burgos Edwards, Alberto Javier | |
| dc.contributor.other | Universidad Católica Nuestra Señora de la Asunción | es |
| dc.contributor.other | Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas | es |
| dc.date.accessioned | 2022-04-06T23:05:55Z | |
| dc.date.available | 2022-04-06T23:05:55Z | |
| dc.date.issued | 2017-12-21 | |
| dc.identifier.citation | Caniza, H., Galeano, D., & Paccanaro, A. (2017, 04-08 de septiembre). Mining the biomedical literature to predict shared drug targets in DrugBank [Artículo de la Conferencia]. 2017 XLIII Latin American Computer Conference (CLEI), Córdoba, Argentina.
https://doi.org/10.1109/CLEI.2017.8226376 | en |
| dc.identifier.isbn | 978-1-5386-3057-0 | es |
| dc.identifier.other | https://doi.org/10.1109/CLEI.2017.8226376 | es |
| dc.identifier.uri | http://hdl.handle.net/20.500.14066/2840 | |
| dc.description.abstract | The 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.sponsorship | Consejo Nacional de Ciencia y Tecnología | es |
| dc.format.extent | 5 páginas | es |
| dc.language.iso | eng | es |
| dc.publisher | Institute of Electrical and Electronics Engineers | en |
| dc.relation.ispartof | 2017 XLIII Latin American Computer Conference (CLEI) | en |
| dc.subject.classification | 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations | en |
| dc.subject.classification | 6. Producción y tecnología industrial | es |
| dc.subject.classification | 8. Agricultura | es |
| dc.subject.classification | 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura | es |
| dc.subject.other | Chemicals | es |
| dc.subject.other | Diseases | es |
| dc.subject.other | Drugs | es |
| dc.subject.other | Indexes | es |
| dc.subject.other | Ontologies | es |
| dc.subject.other | Proteins | es |
| dc.subject.other | Semantics | es |
| dc.title | Mining the biomedical literature to predict shared drug targets in DrugBank | es |
| dc.type | conference paper | es |
| dc.identifier.doi | 10.1109/CLEI.2017.8226376 | es |
| dc.conference.date | 2017-09-04 | es |
| dc.conference.place | Córdoba, AR | es |
| dc.conference.title | 2017 XLIII Latin American Computer Conference (CLEI) | en |
| dc.description.fundingtext | Programa Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarrollo | es |
| dc.relation.projectCONACYT | 14-INV-088 | es |
| dc.rights.accessRights | restricted access | es |
| dc.rights.copyright | © 2017 IEEE European Union | es |
| dc.subject.ocde | 4. Ciencias Agrícolas y Veterinarias | es |
| dc.subject.ocde | 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) | es |
