Mining the biomedical literature to predict shared drug targets in DrugBank. / Caniza Vierci, Horacio; Galeano Galeano, Diego; Paccanaro, Alberto.

XLIII Conferencia Latinoamericana en Informática CLEI 2017. IEEE Xplore, 2017. p. 1-5.

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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
Original languageEnglish
Title of host publicationXLIII Conferencia Latinoamericana en Informática CLEI 2017
PublisherIEEE Xplore
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5386-3057-0
DOIs
StatePublished - 2017
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 28433115