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Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective
With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485337/ https://www.ncbi.nlm.nih.gov/pubmed/28690664 http://dx.doi.org/10.1155/2017/7198645 |
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author | Gu, Shuo Pei, Jianfeng |
author_facet | Gu, Shuo Pei, Jianfeng |
author_sort | Gu, Shuo |
collection | PubMed |
description | With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed. |
format | Online Article Text |
id | pubmed-5485337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-54853372017-07-09 Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective Gu, Shuo Pei, Jianfeng Evid Based Complement Alternat Med Review Article With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed. Hindawi 2017 2017-06-11 /pmc/articles/PMC5485337/ /pubmed/28690664 http://dx.doi.org/10.1155/2017/7198645 Text en Copyright © 2017 Shuo Gu and Jianfeng Pei. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Gu, Shuo Pei, Jianfeng Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective |
title | Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective |
title_full | Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective |
title_fullStr | Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective |
title_full_unstemmed | Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective |
title_short | Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective |
title_sort | chinese herbal medicine meets biological networks of complex diseases: a computational perspective |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485337/ https://www.ncbi.nlm.nih.gov/pubmed/28690664 http://dx.doi.org/10.1155/2017/7198645 |
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