| dc.creator | Caniza, Horacio | |
| dc.creator | Galeano, Diego | |
| dc.creator | Paccanaro, Alberto | |
| dc.date | 2017-09 | |
| dc.date | 2017 | |
| dc.date | 2017-10-26T14:32:43Z | |
| dc.identifier | http://sedici.unlp.edu.ar/handle/10915/63201 | |
| dc.identifier | http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/SLMDI/SLMDI-03.pdf | |
| dc.description | 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. | |
| dc.description | Sociedad Argentina de Informática e Investigación Operativa (SADIO) | |
| dc.format | application/pdf | |
| dc.language | en | |
| dc.rights | http://creativecommons.org/licenses/by-sa/3.0/ | |
| dc.rights | Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) | |
| dc.subject | Ciencias Informáticas | |
| dc.subject | MeSH terms | |
| dc.subject | drug descriptors | |
| dc.subject | drug targets | |
| dc.subject | drugbank | |
| dc.title | Mining the biomedical literature to predict shared drug targets in drugbank | |
| dc.type | Objeto de conferencia | |
| dc.type | Objeto de conferencia | |
