文献类型：期刊文献

英文题名：TCMFVis: A visual analytics system toward bridging together traditional Chinese medicine and modern medicine

作者：Jin, Yichao[1];Zhu, Fuli[2];Li, Jianhua[1];Ma, Lei[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Meilong Rd 130, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Meilong Rd 130, Shanghai, Peoples R China

年份：2023

卷号：7

期号：1

起止页码：41

外文期刊名：VISUAL INFORMATICS

收录：EI(收录号：20231413854379);WOS:【ESCI(收录号:WOS:000980458600001)】；

基金：The work was supported by National Key R and D Program of China (under Grant No. 2016YFA0502304) and Important Drug Development Fund, Ministry of Science and Technology of China (2018ZX09735002) .

语种：英文

外文关键词：Traditional Chinese medicine; Visual analytics; Set visualization; Molecular mechanism

摘要：Although traditional Chinese medicine (TCM) and modern medicine (MM) have considerably different treatment philosophies, they both make important contributions to human health care. TCM physicians usually treat diseases using TCM formula (TCMF), which is a combination of specific herbs, based on the holistic philosophy of TCM, whereas MM physicians treat diseases using chemical drugs that interact with specific biological molecules. The difference between the holistic view of TCM and the atomistic view of MM hinders their combination. Tools that are able to bridge together TCM and MM are essential for promoting the combination of these disciplines. In this paper, we present TCMFVis, a visual analytics system that would help domain experts explore the potential use of TCMFs in MM at the molecular level. TCMFVis deals with two significant challenges, namely, (i) intuitively obtaining valuable insights from heterogeneous data involved in TCMFs and (ii) efficiently identifying the common features among a cluster of TCMFs. In this study, a four-level (herb-ingredient-targetdisease) visual analytics framework was designed to facilitate the analysis of heterogeneous data in a proper workflow. Several set visualization techniques were first introduced into the system to facilitate the identification of common features among TCMFs. Case studies on two groups of TCMFs clustered by function were conducted by domain experts to evaluate TCMFVis. The results of these case studies demonstrate the usability and scalability of the system.(c) 2023 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).