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Linking common human diseases to their phenotypes; development of a resource for human phenomics
BACKGROUND: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8383460/ https://www.ncbi.nlm.nih.gov/pubmed/34425897 http://dx.doi.org/10.1186/s13326-021-00249-x |
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author | Kafkas, Şenay Althubaiti, Sara Gkoutos, Georgios V. Hoehndorf, Robert Schofield, Paul N. |
author_facet | Kafkas, Şenay Althubaiti, Sara Gkoutos, Georgios V. Hoehndorf, Robert Schofield, Paul N. |
author_sort | Kafkas, Şenay |
collection | PubMed |
description | BACKGROUND: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. METHODS: We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships. RESULTS: We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease–phenotype associations, measuring their similarity to disease–phenotype associations found in public databases, and assessing how well they could be used to recover gene–disease associations using phenotype similarity. CONCLUSION: We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at 10.5281/zenodo.4726713. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13326-021-00249-x). |
format | Online Article Text |
id | pubmed-8383460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83834602021-08-25 Linking common human diseases to their phenotypes; development of a resource for human phenomics Kafkas, Şenay Althubaiti, Sara Gkoutos, Georgios V. Hoehndorf, Robert Schofield, Paul N. J Biomed Semantics Research BACKGROUND: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. METHODS: We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships. RESULTS: We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease–phenotype associations, measuring their similarity to disease–phenotype associations found in public databases, and assessing how well they could be used to recover gene–disease associations using phenotype similarity. CONCLUSION: We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at 10.5281/zenodo.4726713. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13326-021-00249-x). BioMed Central 2021-08-23 /pmc/articles/PMC8383460/ /pubmed/34425897 http://dx.doi.org/10.1186/s13326-021-00249-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Research Kafkas, Şenay Althubaiti, Sara Gkoutos, Georgios V. Hoehndorf, Robert Schofield, Paul N. Linking common human diseases to their phenotypes; development of a resource for human phenomics |
title | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
title_full | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
title_fullStr | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
title_full_unstemmed | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
title_short | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
title_sort | linking common human diseases to their phenotypes; development of a resource for human phenomics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8383460/ https://www.ncbi.nlm.nih.gov/pubmed/34425897 http://dx.doi.org/10.1186/s13326-021-00249-x |
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