计算机科学
鉴定(生物学)
中医药
破译
数据科学
网络理论
信息学
网络分析
人工智能
大数据
数据挖掘
工程类
生物信息学
医学
生物
统计
植物
电气工程
病理
数学
替代医学
作者
Shao Li,Boyang Wang,Liang Cao,Li-Hao Xiao,Pan Chen,Bo Zhang,Xinzhuang Zhang,Wei Xiao
出处
期刊:PubMed
[National Institutes of Health]
日期:2023-11-01
卷期号:48 (22): 5965-5976
被引量:8
标识
DOI:10.19540/j.cnki.cjcmm.20230923.701
摘要
Network targets theory and technology have transcended the limitations of the "single gene, single target" model, aiming to decipher the mechanisms of traditional Chinese medicine(TCM) based on biological network from the perspective of informatics and system. As the core of TCM network pharmacology, with the development of computer science and high-throughput experimental techniques, the network target theory and technology are beginning to exhibit a trend of organic integration with artificial intelligence technology and high-throughput multi-modal multi-omics experimental techniques. Taking the network target analysis of TCM like Yinqiao Qingre Tablets as a typical case, network target theory and technology have achieved the systematic construction, in-depth analysis, and high-throughput multi-modal multi-omics validation of multi-level biological networks spanning from traditional Chinese and Western phenotypes to tissues, cells, molecules, and traditional Chinese and Western medicines. This development helps to address critical issues in the analysis of mechanisms of TCM, including the discovery of key targets, identification of functional components, discovery of synergistic effects among compound ingredients, and elucidation of the regulatory mechanisms of formulae. It provides powerful theoretical and technological support for advancing clinical precision diagnosis and treatment, precise positioning of TCM, and precise research and development of TCM. Thus, a new paradigm of TCM research gradually emerges, combining big data and artificial intelligence(AI) with the integration of human experience and scientific evidence.
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