FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.
FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.
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FangNet：利用结构网络算法从中医临床有效方剂中挖掘草药隐藏知识
DOI：
10.1016/j.csbj.2020.11.036
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发表时间：
2021
影响因子：
6
通讯作者：
Zhao Y
中科院分区：
文献类型：
--
作者：
Bu D;Xia Y;Zhang J;Cao W;Huo P;Wang Z;He Z;Ding L;Wu Y;Zhang S;Gao K;Yu H;Liu T;Ding X;Gu X;Zhao Y
使用草药治疗各种人类疾病已经有数千年的记录。在亚洲目前的医疗体系中，无数的草药配方经过反复验证，证实了它们在不同时期的有效性，这是药物创新和发现的巨大资源。通过网络药理学和生物信息学分析挖掘这些临床有效方剂，可能发现这些天然产物中的重要生物活性成分。由于现代医学需要多种药物联合治疗复杂疾病，以前的临床处方也是根据病因和伴随症状将各种药物组合在一起。然而，在治疗疾病中发挥主要作用的草药总是不清楚。因此，如何对各中草药的相对重要性进行排序，确定核心中草药，是辅助中草药活性成分筛选的第一步。为了解决这个问题，我们建立了FangNet平台，该平台使用PageRank算法对所有草药的相对拓扑重要性进行排名，基于从临床经验处方集合中构建的草药网络。三种类型的草药隐藏的知识，包括草药的重要性排名，草药的共同出现，和协会的症状，提供了一个互动的可视化。此外，FangNet还为团队设计了基于角色的权限，以便在一个简单安全的协作环境中存储，分析和共同解释他们的临床配方，旨在为大规模的草药连接创建一个中心枢纽。FangNet可以在http://fangnet.org或http://fangnet.herb.ac.cn上访问。
The use of herbs to treat various human diseases has been recorded for thousands of years. In Asia's current medical system, numerous herbal formulas have been repeatedly verified to confirm their effectiveness in different periods, which is a great resource for drug innovation and discovery. Through the mining of these clinical effective formulas by network pharmacology and bioinformatics analysis, important biologically active ingredients derived from these natural products might be discovered. As modern medicine requires a combination of multiple drugs for the treatment of complex diseases, previously clinical formulas are also combinations of various herbs according to the main causes and accompanying symptoms. However, the herbs that play a major role in the treatment of diseases are always unclear. Therefore, how to rank each herb's relative importance and determine the core herbs, is the first step to assisting herb selection for active ingredients discovery. To solve this problem, we built the platform FangNet, which ranks all herbs on their relative topological importance using the PageRank algorithm, based on the constructed symptom-herb network from a collection of clinical empirical prescriptions. Three types of herb hidden knowledge, including herb importance rank, herb-herb co-occurrence, and associations to symptoms, were provided in an interactive visualization. Moreover, FangNet has designed role-based permission for teams to store, analyze, and jointly interpret their clinical formulas, in an easy and secure collaboration environment, aiming at creating a central hub for massive symptom-herb connections. FangNet can be accessed at http://fangnet.org or http://fangnet.herb.ac.cn.
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DOI：
10.1155/2013/125943
发表时间：
2013
期刊：
Evidence-based complementary and alternative medicine : eCAM
影响因子：
--
作者：
Chen HY;Lin YH;Thien PF;Chang SC;Chen YC;Lo SS;Yang SH;Chen JL
通讯作者：
Chen JL
治疗性增强和进步的草药结合：理论，实践和未来观点。
DOI：
10.3390/molecules18055125
发表时间：
2013-05-03
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Che CT;Wang ZJ;Chow MS;Lam CW
通讯作者：
Lam CW
DOI：
10.1093/ecam/nel001
发表时间：
2006-03
期刊：
Evidence-based complementary and alternative medicine : eCAM
影响因子：
--
作者：
Adams LS;Seeram NP;Hardy ML;Carpenter C;Heber D
通讯作者：
Heber D
影响因子：
3
作者：
Cragg, Gordon M.;Newman, David J.
通讯作者：
Newman, David J.
影响因子：
5.4
作者：
Chu, Shih-Meng;Shih, Wei-Tai;Chu, Yen-Hua
通讯作者：
Chu, Yen-Hua