中医药
管理科学
计算机科学
集合(抽象数据类型)
透视图(图形)
传统医学
人工智能
数据科学
医学
工程类
替代医学
病理
程序设计语言
作者
Yuehua Chen,Jing-Hua Bi,Ming Xie,Hui Zhang,Zi‐Qi Shi,Hua Guo,Haibo Yin,Jia-Nuo Zhang,Gui-Zhong Xin,Hui‐Peng Song
标识
DOI:10.1016/j.chroma.2021.462307
摘要
The difficulty of traditional Chinese medicine (TCM) researches lies in the complexity of components, metabolites, and bioactivities. For a long time, there has been a lack of connections among the three parts, which is not conducive to the systematic elucidation of TCM effectiveness. To overcome this problem, a classification-based methodology for simplifying TCM researches was refined from literature in the past 10 years (2011–2020). The theoretical basis of this methodology is set theory, and its core concept is classification. Its starting point is that "although TCM may contain hundreds of compounds, the vast majority of these compounds are structurally similar". The methodology is composed by research strategies for components, metabolites and bioactivities of TCM, which are the three main parts of the review. Technical route, key steps and difficulty are introduced in each part. Two perspectives are highlighted in this review: set theory is a theoretical basis for all strategies from a conceptual perspective, and liquid chromatography-mass spectrometry (LC-MS) is a common tool for all strategies from a technical perspective. The significance of these strategies is to simplify complex TCM researches, integrate isolated TCM researches, and build a bridge between traditional medicines and modern medicines. Potential research hotspots in the future, such as discovery of bioactive ingredients from TCM metabolites, are also discussed. The classification-based methodology is a summary of research experience in the past 10 years. We believe it will definitely provide support and reference for the following TCM researches.
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