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Mining a stroke knowledge graph from literature

BACKGROUND: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the “Western” biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often st...

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Autores principales: Yang, Xi, Wu, Chengkun, Nenadic, Goran, Wang, Wei, Lu, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319697/
https://www.ncbi.nlm.nih.gov/pubmed/34325669
http://dx.doi.org/10.1186/s12859-021-04292-4
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author Yang, Xi
Wu, Chengkun
Nenadic, Goran
Wang, Wei
Lu, Kai
author_facet Yang, Xi
Wu, Chengkun
Nenadic, Goran
Wang, Wei
Lu, Kai
author_sort Yang, Xi
collection PubMed
description BACKGROUND: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the “Western” biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases. RESULTS: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine. CONCLUSIONS: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04292-4.
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spelling pubmed-83196972021-07-29 Mining a stroke knowledge graph from literature Yang, Xi Wu, Chengkun Nenadic, Goran Wang, Wei Lu, Kai BMC Bioinformatics Research BACKGROUND: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the “Western” biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases. RESULTS: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine. CONCLUSIONS: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04292-4. BioMed Central 2021-07-29 /pmc/articles/PMC8319697/ /pubmed/34325669 http://dx.doi.org/10.1186/s12859-021-04292-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (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
Yang, Xi
Wu, Chengkun
Nenadic, Goran
Wang, Wei
Lu, Kai
Mining a stroke knowledge graph from literature
title Mining a stroke knowledge graph from literature
title_full Mining a stroke knowledge graph from literature
title_fullStr Mining a stroke knowledge graph from literature
title_full_unstemmed Mining a stroke knowledge graph from literature
title_short Mining a stroke knowledge graph from literature
title_sort mining a stroke knowledge graph from literature
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319697/
https://www.ncbi.nlm.nih.gov/pubmed/34325669
http://dx.doi.org/10.1186/s12859-021-04292-4
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