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中医挖掘者：一个用于挖掘和可视化中药方剂的R包及闪亮应用程序。

SinoMedminer: an R package and shiny application for mining and visualizing traditional Chinese medicine herbal formulas.

作者信息

Dan Wenchao, Guo Xinyuan, Zhang Guangzhong, Zhang Hui, Liu Jin, Li Qiushuang, Chen Yang, He Qingyong

机构信息

Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Xicheng District, Beixian Pavilion Street No. 5, Beijing, 100053, China.

Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

出版信息

Chin Med. 2025 Jun 6;20(1):80. doi: 10.1186/s13020-025-01127-9.

DOI:10.1186/s13020-025-01127-9
PMID:40481553
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12142817/
Abstract

This study addresses limitations of mainstream approaches in traditional Chinese medicine (TCM) data mining by developing the SinoMedminer R package and its Shiny web application. The R package's core functionalities include data cleaning, transformation, TCM attribute statistics, association rule exploration and analysis, clustering analysis, co-occurrence network analysis, formula similarity analysis, formula identification, and dosage analysis. This package enables efficient project analyses without requiring complex coding. The accompanying Shiny web application provides an interactive, menu-driven interface for users without programming knowledge. SinoMedminer combines the computational power of a programming language with user-friendly accessibility, significantly enhancing the efficiency and standardization of TCM data mining research. A deployed server platform further simplifies access and usability by allowing direct utilization of the Shiny application. By optimizing data processing and analysis workflows, SinoMedminer enhances big data handling capabilities, accelerates research progress and product development, and promotes the integration of digital technologies into TCM research and clinical practice.

摘要

本研究通过开发SinoMedminer R包及其Shiny网络应用程序，解决了中医（TCM）数据挖掘中主流方法的局限性。该R包的核心功能包括数据清理、转换、中医属性统计、关联规则探索与分析、聚类分析、共现网络分析、方剂相似度分析、方剂识别和剂量分析。此包无需复杂编码即可实现高效的项目分析。配套的Shiny网络应用程序为没有编程知识的用户提供了一个交互式的、菜单驱动的界面。SinoMedminer将编程语言的计算能力与用户友好的可访问性相结合，显著提高了中医数据挖掘研究的效率和标准化。一个已部署的服务器平台通过允许直接使用Shiny应用程序，进一步简化了访问和可用性。通过优化数据处理和分析工作流程，SinoMedminer增强了大数据处理能力，加速了研究进展和产品开发，并促进了数字技术融入中医研究和临床实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/691bf6a1f5b6/13020_2025_1127_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/2cfc60d3cf9a/13020_2025_1127_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/b4daf8862be0/13020_2025_1127_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/b85dde5347ed/13020_2025_1127_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/7fd933782c95/13020_2025_1127_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/1c9e78df241c/13020_2025_1127_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/691bf6a1f5b6/13020_2025_1127_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/2cfc60d3cf9a/13020_2025_1127_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/4970f0db1be5/13020_2025_1127_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/8e4d8647c02e/13020_2025_1127_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/b4daf8862be0/13020_2025_1127_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/b85dde5347ed/13020_2025_1127_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/7fd933782c95/13020_2025_1127_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/1c9e78df241c/13020_2025_1127_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4e7/12142817/691bf6a1f5b6/13020_2025_1127_Fig8_HTML.jpg

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