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Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources
BACKGROUND: Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are a prime candidate for representation in ontologie...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223332/ https://www.ncbi.nlm.nih.gov/pubmed/28069052 http://dx.doi.org/10.1186/s13326-016-0110-0 |
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author | Sharp, Mark E |
author_facet | Sharp, Mark E |
author_sort | Sharp, Mark E |
collection | PubMed |
description | BACKGROUND: Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are a prime candidate for representation in ontologies. There is a wealth of available drug-indication information, but structuring and integrating it is challenging. RESULTS: We created a drug-indication database (DID) of data from 12 openly available, commercially available, and proprietary information sources, integrated by terminological normalization to UMLS and other authorities. Across sources, there are 29,964 unique raw drug/chemical names, 10,938 unique raw indication ”target” terms, and 192,008 unique raw drug-indication pairs. Drug/chemical name normalization to CAS numbers or UMLS concepts reduced the unique name count to 91 or 85% of the raw count, respectively, 84% if combined. Indication ”target” normalization to UMLS ”phenotypic-type” concepts reduced the unique term count to 57% of the raw count. The 12 sources of raw data varied widely in coverage (numbers of unique drug/chemical and indication concepts and relations) generally consistent with the idiosyncrasies of each source, but had strikingly little overlap, suggesting that we successfully achieved source/raw data diversity. CONCLUSIONS: The DID is a database of structured drug-indication relations intended to facilitate building practical, comprehensive, integrated drug ontologies. The DID itself is not an ontology, but could be converted to one more easily than the contributing raw data. Our methodology could be adapted to the creation of other structured drug-disease databases such as for contraindications, precautions, warnings, and side effects. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13326-016-0110-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5223332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52233322017-01-11 Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources Sharp, Mark E J Biomed Semantics Research BACKGROUND: Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are a prime candidate for representation in ontologies. There is a wealth of available drug-indication information, but structuring and integrating it is challenging. RESULTS: We created a drug-indication database (DID) of data from 12 openly available, commercially available, and proprietary information sources, integrated by terminological normalization to UMLS and other authorities. Across sources, there are 29,964 unique raw drug/chemical names, 10,938 unique raw indication ”target” terms, and 192,008 unique raw drug-indication pairs. Drug/chemical name normalization to CAS numbers or UMLS concepts reduced the unique name count to 91 or 85% of the raw count, respectively, 84% if combined. Indication ”target” normalization to UMLS ”phenotypic-type” concepts reduced the unique term count to 57% of the raw count. The 12 sources of raw data varied widely in coverage (numbers of unique drug/chemical and indication concepts and relations) generally consistent with the idiosyncrasies of each source, but had strikingly little overlap, suggesting that we successfully achieved source/raw data diversity. CONCLUSIONS: The DID is a database of structured drug-indication relations intended to facilitate building practical, comprehensive, integrated drug ontologies. The DID itself is not an ontology, but could be converted to one more easily than the contributing raw data. Our methodology could be adapted to the creation of other structured drug-disease databases such as for contraindications, precautions, warnings, and side effects. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13326-016-0110-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-10 /pmc/articles/PMC5223332/ /pubmed/28069052 http://dx.doi.org/10.1186/s13326-016-0110-0 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Sharp, Mark E Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
title | Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
title_full | Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
title_fullStr | Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
title_full_unstemmed | Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
title_short | Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
title_sort | toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223332/ https://www.ncbi.nlm.nih.gov/pubmed/28069052 http://dx.doi.org/10.1186/s13326-016-0110-0 |
work_keys_str_mv | AT sharpmarke towardacomprehensivedrugontologyextractionofdrugindicationrelationsfromdiverseinformationsources |