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Cited by 675

The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Open access
Zhang, Peng, Zhang, Dingfan, Zhou, Wuai, Wang, Lan, Wang, Boyang, Zhang, Tingyu, Li, Shao·2023·Briefings in Bioinformatics

Abstract

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

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Metadata

Authors
Zhang, P., Zhang, D., Zhou, W., Wang, L., Wang, B., Zhang, T., & Li, S.
Venue
Briefings in Bioinformatics
Published
2023-11-22
Volume
25
Issue
1
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
OpenAlex
W4390711247
Cited by
675
References
101
FWCI
88.97
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. https://app.cochat.ai/papers/10.1093/bib/bbad518
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Zhang, P., Zhang, D., Zhou, W., Wang, L., Wang, B., Zhang, T., & Li, S. (2023). Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbad518

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.