Mining the biomedical literature to predict shared drug targets in DrugBank

Horacio Caniza Vierci; Diego Galeano Galeano; Alberto Paccanaro

doi:10.1109/CLEI.2017.8226376

Mining the biomedical literature to predict shared drug targets in DrugBank

Horacio Caniza Vierci, Diego Galeano Galeano, Alberto Paccanaro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English
Title of host publication	XLIII Conferencia Latinoamericana en Informática CLEI 2017
Publisher	IEEE Xplore
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-5386-3057-0
DOIs	https://doi.org/10.1109/CLEI.2017.8226376
Publication status	Published - 2017

Keywords

MeSH terms, drug descriptors, drug targets, drugbank

Access to Document

10.1109/CLEI.2017.8226376

Cite this

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title = "Mining the biomedical literature to predict shared drug targets in DrugBank",

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",

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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

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BT - XLIII Conferencia Latinoamericana en Informática CLEI 2017

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