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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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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. |
format | Online Article Text |
id | pubmed-5051953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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