MicrobeTCM: A comprehensive platform for the interactions of microbiota and traditional Chinese medicine

被引:2
|
作者
Chen, Yufeng [1 ]
Shi, Yu [1 ]
Liang, Chengbang [1 ]
Min, Zhuochao [2 ,3 ]
Deng, Qiqi [1 ]
Yu, Rui [1 ]
Zhang, Jiani [1 ]
Chang, Kexin [1 ]
Chen, Luyao [1 ]
Yan, Ke [1 ]
Wang, Chunxiang [1 ]
Tan, Yan [1 ]
Wang, Xu [1 ]
Chen, Jianxin [1 ,4 ]
Hua, Qian [1 ,4 ]
机构
[1] Beijing Univ Chinese Med, Sch Tradit Chinese Med, Sch Life Sci, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China
[3] Tel Aviv Univ, Sch Zool, George S Wise Fac Life Sci Tel Aviv, IL-69978 Tel Aviv, Israel
[4] Beijing Univ Chinese Med, Sch Life Sci, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Traditional Chinese medicine; Microbe; Database; Herb; Acupoint; Gut-brain axis; GUT MICROBIOTA; MOUSE MODEL; ACUPUNCTURE;
D O I
10.1016/j.phrs.2024.107080
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupointformulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com.
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页数:11
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