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Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine
AIM: We evaluated the developmental process, research status, and existing challenges of network pharmacology. Moreover, we elucidated the corresponding solutions to improve and develop network pharmacology. METHODS: Research data for the current study were retrieved from the Web of Science. The dev...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217600/ https://www.ncbi.nlm.nih.gov/pubmed/35754692 http://dx.doi.org/10.1155/2022/1583773 |
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author | Miao, Runpei Meng, Qinggang Wang, Chennan Yuan, Wenjing |
author_facet | Miao, Runpei Meng, Qinggang Wang, Chennan Yuan, Wenjing |
author_sort | Miao, Runpei |
collection | PubMed |
description | AIM: We evaluated the developmental process, research status, and existing challenges of network pharmacology. Moreover, we elucidated the corresponding solutions to improve and develop network pharmacology. METHODS: Research data for the current study were retrieved from the Web of Science. The developmental process of network pharmacology was analyzed using HisCite, whereas cooccurrence analysis of countries, institutions, keywords, and references in literature was conducted using CiteSpace. RESULTS: In literature, there was a trend of annual increase of studies on network pharmacology and China was found to be the country with the most published literature on network pharmacology. The main publishing research institutions were universities of traditional Chinese medicine (TCM). The keywords with more research frequency were TCM, mechanisms, molecular docking, and quercetin, among others. CONCLUSION: Currently, studies on network pharmacology are mainly associated with the exploration of action mechanisms of TCM. The main active ingredient in many Chinese medicines is quercetin. This ingredient may lead to deviation of research results, inability to truly analyze active ingredients, and even mislead the research direction of TCM. Such deviation may be because the database fails to reflect the content and composition changes of Chinese medicinal components. The database does not account for interactions among components, targets, and diseases, and it ignores the different pathological states of the disease. Therefore, network pharmacology should be improved from the databases and research methods. |
format | Online Article Text |
id | pubmed-9217600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92176002022-06-23 Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine Miao, Runpei Meng, Qinggang Wang, Chennan Yuan, Wenjing Evid Based Complement Alternat Med Research Article AIM: We evaluated the developmental process, research status, and existing challenges of network pharmacology. Moreover, we elucidated the corresponding solutions to improve and develop network pharmacology. METHODS: Research data for the current study were retrieved from the Web of Science. The developmental process of network pharmacology was analyzed using HisCite, whereas cooccurrence analysis of countries, institutions, keywords, and references in literature was conducted using CiteSpace. RESULTS: In literature, there was a trend of annual increase of studies on network pharmacology and China was found to be the country with the most published literature on network pharmacology. The main publishing research institutions were universities of traditional Chinese medicine (TCM). The keywords with more research frequency were TCM, mechanisms, molecular docking, and quercetin, among others. CONCLUSION: Currently, studies on network pharmacology are mainly associated with the exploration of action mechanisms of TCM. The main active ingredient in many Chinese medicines is quercetin. This ingredient may lead to deviation of research results, inability to truly analyze active ingredients, and even mislead the research direction of TCM. Such deviation may be because the database fails to reflect the content and composition changes of Chinese medicinal components. The database does not account for interactions among components, targets, and diseases, and it ignores the different pathological states of the disease. Therefore, network pharmacology should be improved from the databases and research methods. Hindawi 2022-06-15 /pmc/articles/PMC9217600/ /pubmed/35754692 http://dx.doi.org/10.1155/2022/1583773 Text en Copyright © 2022 Runpei Miao et al. 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 | Research Article Miao, Runpei Meng, Qinggang Wang, Chennan Yuan, Wenjing Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine |
title | Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine |
title_full | Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine |
title_fullStr | Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine |
title_full_unstemmed | Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine |
title_short | Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine |
title_sort | bibliometric analysis of network pharmacology in traditional chinese medicine |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217600/ https://www.ncbi.nlm.nih.gov/pubmed/35754692 http://dx.doi.org/10.1155/2022/1583773 |
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