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pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

BACKGROUND: Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become im...

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Autores principales: Luo, Zhi-Hui, Shi, Meng-Wei, Yang, Zhuang, Zhang, Hong-Yu, Chen, Zhen-Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301509/
https://www.ncbi.nlm.nih.gov/pubmed/32552728
http://dx.doi.org/10.1186/s12859-020-03583-6
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author Luo, Zhi-Hui
Shi, Meng-Wei
Yang, Zhuang
Zhang, Hong-Yu
Chen, Zhen-Xia
author_facet Luo, Zhi-Hui
Shi, Meng-Wei
Yang, Zhuang
Zhang, Hong-Yu
Chen, Zhen-Xia
author_sort Luo, Zhi-Hui
collection PubMed
description BACKGROUND: Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis. RESULTS: The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall > 0.94, precision > 0.56, and F1 > 0.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89–0.99. CONCLUSIONS: The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining.
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spelling pubmed-73015092020-06-18 pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms Luo, Zhi-Hui Shi, Meng-Wei Yang, Zhuang Zhang, Hong-Yu Chen, Zhen-Xia BMC Bioinformatics Software BACKGROUND: Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis. RESULTS: The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall > 0.94, precision > 0.56, and F1 > 0.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89–0.99. CONCLUSIONS: The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining. BioMed Central 2020-06-18 /pmc/articles/PMC7301509/ /pubmed/32552728 http://dx.doi.org/10.1186/s12859-020-03583-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Software
Luo, Zhi-Hui
Shi, Meng-Wei
Yang, Zhuang
Zhang, Hong-Yu
Chen, Zhen-Xia
pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
title pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
title_full pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
title_fullStr pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
title_full_unstemmed pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
title_short pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
title_sort pymeshsim: an integrative python package for biomedical named entity recognition, normalization, and comparison of mesh terms
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301509/
https://www.ncbi.nlm.nih.gov/pubmed/32552728
http://dx.doi.org/10.1186/s12859-020-03583-6
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