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PubMedPortable: A Framework for Supporting the Development of Text Mining Applications

Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified enti...

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Detalles Bibliográficos
Autores principales: Döring, Kersten, Grüning, Björn A., Telukunta, Kiran K., Thomas, Philippe, Günther, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051953/
https://www.ncbi.nlm.nih.gov/pubmed/27706202
http://dx.doi.org/10.1371/journal.pone.0163794
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author Döring, Kersten
Grüning, Björn A.
Telukunta, Kiran K.
Thomas, Philippe
Günther, Stefan
author_facet Döring, Kersten
Grüning, Björn A.
Telukunta, Kiran K.
Thomas, Philippe
Günther, Stefan
author_sort Döring, Kersten
collection PubMed
description Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user’s system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.
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spelling pubmed-50519532016-10-27 PubMedPortable: A Framework for Supporting the Development of Text Mining Applications Döring, Kersten Grüning, Björn A. Telukunta, Kiran K. Thomas, Philippe Günther, Stefan PLoS One Research Article Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user’s system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects. Public Library of Science 2016-10-05 /pmc/articles/PMC5051953/ /pubmed/27706202 http://dx.doi.org/10.1371/journal.pone.0163794 Text en © 2016 Döring et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Döring, Kersten
Grüning, Björn A.
Telukunta, Kiran K.
Thomas, Philippe
Günther, Stefan
PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
title PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
title_full PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
title_fullStr PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
title_full_unstemmed PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
title_short PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
title_sort pubmedportable: a framework for supporting the development of text mining applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051953/
https://www.ncbi.nlm.nih.gov/pubmed/27706202
http://dx.doi.org/10.1371/journal.pone.0163794
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