RT Journal Article
T1 Mining the biomedical literature to predict shared drug targets in DrugBank
A1 Caniza, Horacio Jose
A1 Galeano Galeano, Diego Ariel
A1 Paccanaro, Alberto
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.
YR 2017
FD 2017
LK http://hdl.handle.net/20.500.14066/2840
UL http://hdl.handle.net/20.500.14066/2840
LA eng
NO CONACYT – Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 26-jun-2024