IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v460y2016icp152-161.html
   My bibliography  Save this article

Complex network model of the Treatise on Cold Damage Disorders

Author

Listed:
  • Shao, Feng-jing
  • Sui, Yi
  • Zhou, Yong-hong
  • Sun, Ren-cheng

Abstract

Investigating the underlying principles of the Treatise on Cold Damage Disorder is meaningful and interesting. In this study, we investigated the symptoms, herbal formulae, herbal drugs, and their relationships in this treatise based on a multi-subnet composited complex network model (MCCN). Syndrome subnets were constructed for the symptoms and a formula subnet for herbal drugs. By subnet compounding using MCCN, a composited network was obtained that described the treatment relationships between syndromes and formulae. The results obtained by topological analysis suggested some prescription laws that could be validated in clinics. After subnet reduction using the MCCN, six channel (Tai-yang, Yang-ming, Shao-yang, Tai-yin, Shao-yin, and Jue-yin) subnets were obtained. By analyzing the strengths of the relationships among these six channel subnets, we found that the Tai-yang channel and Yang-ming channel were related most strongly with each other, and we found symptoms that implied pathogen movements and transformations among the six channels. This study could help therapists to obtain a deeper understanding of this ancient treatise.

Suggested Citation

  • Shao, Feng-jing & Sui, Yi & Zhou, Yong-hong & Sun, Ren-cheng, 2016. "Complex network model of the Treatise on Cold Damage Disorders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 152-161.
    • Handle: RePEc:eee:phsmap:v:460:y:2016:i:c:p:152-161
    DOI: 10.1016/j.physa.2016.03.115
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116301133
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.03.115?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fengjing Shao & Yi Sui, 2014. "Reorganizations of complex networks: Compounding and reducing," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(05), pages 1-11.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matsuzawa, Ryo & Tanimoto, Jun & Fukuda, Eriko, 2017. "Properties of a new small-world network with spatially biased random shortcuts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 408-415.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sui, Yi & Shao, Fengjing & Wang, Changying & Sun, Rencheng & Ji, Jun, 2016. "Complex network modeling of spectral remotely sensed imagery: A case study of massive green algae blooms detection based on MODIS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 138-148.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:460:y:2016:i:c:p:152-161. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.