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

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yichao Jin , Fuli Zhu , Jianhua Li , Lei Ma
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Abstract

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-target-disease) 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.

TCMFVis:一个连接传统中医和现代医学的可视化分析系统
尽管传统中医和现代医学有着截然不同的治疗理念,但它们都对人类健康做出了重要贡献。中医医生通常使用中医配方(TCMF)治疗疾病,TCMF是基于中医整体哲学的特定草药的组合,而MM医生使用与特定生物分子相互作用的化学药物治疗疾病。中医整体观与MM原子观的差异阻碍了两者的结合。能够将中医和MM联系在一起的工具对于促进这些学科的结合至关重要。在本文中,我们介绍了TCMFVis,这是一个视觉分析系统,将帮助领域专家在分子水平上探索TCMFs在MM中的潜在用途。TCMFVis处理了两个重大挑战,即(i)从TCMFs中涉及的异构数据中直观地获得有价值的见解,以及(ii)有效地识别TCMFs集群中的共同特征。在这项研究中,设计了一个四级(草药成分-靶向疾病)视觉分析框架,以便于在适当的工作流程中分析异构数据。系统中首先引入了几种集合可视化技术,以便于识别TCMF之间的共同特征。领域专家对两组按功能聚类的TCMFs进行了案例研究,以评估TCMFVis。这些案例研究的结果证明了该系统的可用性和可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
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