Approximate subgraph matching-based literature mining for biomedical events and relations.

The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biom...

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Autores principales:	Haibin Liu, Lawrence Hunter, Vlado Kešelj, Karin Verspoor
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/995f7980536a4081ba98d97a1934f428
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id	oai:doaj.org-article:995f7980536a4081ba98d97a1934f428
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spelling	oai:doaj.org-article:995f7980536a4081ba98d97a1934f4282021-11-18T07:49:09ZApproximate subgraph matching-based literature mining for biomedical events and relations.1932-620310.1371/journal.pone.0060954https://doaj.org/article/995f7980536a4081ba98d97a1934f4282013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23613763/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biomedical literature using approximate subgraph matching. Extraction of such knowledge is performed by searching for an approximate subgraph isomorphism between key contextual dependencies and input sentence graphs. Our approach significantly increases the chance of retrieving relations or events encoded within complex dependency contexts by introducing error tolerance into the graph matching process, while maintaining the extraction precision at a high level. When evaluated on practical tasks, it achieves a 51.12% F-score in extracting nine types of biological events on the GE task of the BioNLP-ST 2011 and an 84.22% F-score in detecting protein-residue associations. The performance is comparable to the reported systems across these tasks, and thus demonstrates the generalizability of our proposed approach.Haibin LiuLawrence HunterVlado KešeljKarin VerspoorPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e60954 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haibin Liu Lawrence Hunter Vlado Kešelj Karin Verspoor Approximate subgraph matching-based literature mining for biomedical events and relations.
description	The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biomedical literature using approximate subgraph matching. Extraction of such knowledge is performed by searching for an approximate subgraph isomorphism between key contextual dependencies and input sentence graphs. Our approach significantly increases the chance of retrieving relations or events encoded within complex dependency contexts by introducing error tolerance into the graph matching process, while maintaining the extraction precision at a high level. When evaluated on practical tasks, it achieves a 51.12% F-score in extracting nine types of biological events on the GE task of the BioNLP-ST 2011 and an 84.22% F-score in detecting protein-residue associations. The performance is comparable to the reported systems across these tasks, and thus demonstrates the generalizability of our proposed approach.
format	article
author	Haibin Liu Lawrence Hunter Vlado Kešelj Karin Verspoor
author_facet	Haibin Liu Lawrence Hunter Vlado Kešelj Karin Verspoor
author_sort	Haibin Liu
title	Approximate subgraph matching-based literature mining for biomedical events and relations.
title_short	Approximate subgraph matching-based literature mining for biomedical events and relations.
title_full	Approximate subgraph matching-based literature mining for biomedical events and relations.
title_fullStr	Approximate subgraph matching-based literature mining for biomedical events and relations.
title_full_unstemmed	Approximate subgraph matching-based literature mining for biomedical events and relations.
title_sort	approximate subgraph matching-based literature mining for biomedical events and relations.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/995f7980536a4081ba98d97a1934f428
work_keys_str_mv	AT haibinliu approximatesubgraphmatchingbasedliteratureminingforbiomedicaleventsandrelations AT lawrencehunter approximatesubgraphmatchingbasedliteratureminingforbiomedicaleventsandrelations AT vladokeselj approximatesubgraphmatchingbasedliteratureminingforbiomedicaleventsandrelations AT karinverspoor approximatesubgraphmatchingbasedliteratureminingforbiomedicaleventsandrelations
_version_	1718422927808921600

Approximate subgraph matching-based literature mining for biomedical events and relations.

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