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SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptom...

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Detalles Bibliográficos
Autores principales: Wu, Yang, Zhang, Feilong, Yang, Kuo, Fang, Shuangsang, Bu, Dechao, Li, Hui, Sun, Liang, Hu, Hairuo, Gao, Kuo, Wang, Wei, Zhou, Xuezhong, Zhao, Yi, Chen, Jianxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323958/
https://www.ncbi.nlm.nih.gov/pubmed/30380087
http://dx.doi.org/10.1093/nar/gky1021
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author Wu, Yang
Zhang, Feilong
Yang, Kuo
Fang, Shuangsang
Bu, Dechao
Li, Hui
Sun, Liang
Hu, Hairuo
Gao, Kuo
Wang, Wei
Zhou, Xuezhong
Zhao, Yi
Chen, Jianxin
author_facet Wu, Yang
Zhang, Feilong
Yang, Kuo
Fang, Shuangsang
Bu, Dechao
Li, Hui
Sun, Liang
Hu, Hairuo
Gao, Kuo
Wang, Wei
Zhou, Xuezhong
Zhao, Yi
Chen, Jianxin
author_sort Wu, Yang
collection PubMed
description Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.
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spelling pubmed-63239582019-01-10 SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping Wu, Yang Zhang, Feilong Yang, Kuo Fang, Shuangsang Bu, Dechao Li, Hui Sun, Liang Hu, Hairuo Gao, Kuo Wang, Wei Zhou, Xuezhong Zhao, Yi Chen, Jianxin Nucleic Acids Res Database Issue Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap. Oxford University Press 2019-01-08 2018-10-31 /pmc/articles/PMC6323958/ /pubmed/30380087 http://dx.doi.org/10.1093/nar/gky1021 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Database Issue
Wu, Yang
Zhang, Feilong
Yang, Kuo
Fang, Shuangsang
Bu, Dechao
Li, Hui
Sun, Liang
Hu, Hairuo
Gao, Kuo
Wang, Wei
Zhou, Xuezhong
Zhao, Yi
Chen, Jianxin
SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
title SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
title_full SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
title_fullStr SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
title_full_unstemmed SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
title_short SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping
title_sort symmap: an integrative database of traditional chinese medicine enhanced by symptom mapping
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323958/
https://www.ncbi.nlm.nih.gov/pubmed/30380087
http://dx.doi.org/10.1093/nar/gky1021
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