datasheet1_Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions.docx (2.05 MB)
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datasheet1_Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions.docx

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posted on 20.01.2021, 04:09 authored by Ning Wang, Ninglin Du, Yonghong Peng, Kuo Yang, Zixin Shu, Kai Chang, Di Wu, Jian Yu, Caiyan Jia, Yana Zhou, Xiaodong Li, Baoyan Liu, Zhuye Gao, Runshun Zhang, Xuezhong Zhou

As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called “Jun-Chen-Zuo-Shi” in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.

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