FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm

doi:10.1016/j.csbj.2020.11.036

	FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm
	Bu, Dechao 1; Xia, Yan 3; Zhang, JiaYuan 3; Cao, Wanchen 3; Huo, Peipei 2; Wang, Zhihao 2; He, Zihao 3; Ding, Linyi 3; Wu, Yang 1; Zhang, Shan 2
刊名	COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
	2021
卷号	19 页码:62-71
关键词	Formulas Herb Symptom TCM
ISSN号	2001-0370
DOI	10.1016/j.csbj.2020.11.036
英文摘要	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. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
资助项目	National Key R&D Program of China[2018YFC1704100] ; National Key R&D Program of China[2019YFC1709801] ; National Natural Science Foundation for Young Scholars of China[31701149] ; National Natural Science Foundation for Young Scholars of China[31701141] ; National Natural Science Foundation of China[31672126] ; National Natural Science Foundation of China[32070670] ; National Natural Science Foundation of Zhejiang Province[LY20C060001] ; National Natural Science Foundation of Zhejiang Province[LY21C060003]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000692605200005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17167]
专题	中国科学院计算技术研究所
通讯作者	Ding, Xia; Gu, Xiaohong; Zhao, Yi
作者单位	1.Chinese Acad Sci, Adv Comp Res Ctr, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Luoyang Branch, Luoyang, Peoples R China 3.Beijing Univ Chinese Med, Beijing 100029, Peoples R China
推荐引用方式 GB/T 7714	Bu, Dechao,Xia, Yan,Zhang, JiaYuan,et al. FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm[J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,2021,19:62-71.
APA	Bu, Dechao.,Xia, Yan.,Zhang, JiaYuan.,Cao, Wanchen.,Huo, Peipei.,...&Zhao, Yi.(2021).FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,19,62-71.
MLA	Bu, Dechao,et al."FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm".COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 19(2021):62-71.