RT info:eu-repo/semantics/conferencePaper
T1 Mining the biomedical literature to predict shared drug targets in DrugBank
A1 Caniza Vierci, Horacio José
A1 Galeano Galeano, Diego Ariel
A1 Burgos Edwards, Alberto Javier
A2 Universidad Católica Nuestra Señora de la Asunción
A2 Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas
AB 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.
PB Institute of Electrical and Electronics Engineers
SN 978-1-5386-3057-0
YR 2017
FD 2017-12-21
LK http://hdl.handle.net/20.500.14066/2840
UL http://hdl.handle.net/20.500.14066/2840
LA eng
NO 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
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 03-sep-2026