Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Author:
Description:
Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the ...
Publisher:
Helsingin yliopisto ; Helsingfors universitet ; University of Helsinki
Contributors:
University of Helsinki, Faculty of Biological and Environmental Sciences ; Doctoral Programme in Integrative Life Science ; Helsingin yliopisto, bio- ja ympäristötieteellinen tiedekunta ; Integroivien biotieteiden tohtoriohjelma ; Helsingfors universitet, bio- och miljövetenskapliga fakulteten ; Doktorandprogrammet i integrerande biovetenskap ; Cheng, Feixiong ; Tang, Jing
Year of Publication:
2021-10-06T11:51:18Z
Document Type:
Doctoral dissertation (article-based) ; Artikkeliväitöskirja ; Artikelavhandling ; Text ; 1182 Biokemia, solu- ja molekyylibiologia ; 11831 Kasvibiologia ; 3111 Biolääketieteet ; 3121 Yleislääketiede, sisätaudit ja muut kliiniset lääketieteet ; 1182 Biokemi, cell- och molekylärbiologi ; 11831 Växtbiologi ; 3111 Biomedicinska vetenskaper ; 3121 Allmänmedicin, inre medicin och annan klinisk medicin ; 1182 Biochemistry, cell and molecular biology ; 11831 Plant biology ; 3111 Biomedicine ; 3121 General medicine, internal medicine and other clinical medicine ; doctoralThesis ; [Doctoral and postdoctoral thesis]
Language:
eng
Subjects:
bioinformatics
Rights:
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. ; This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. ; Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden.
Relations:
URN:ISBN:978-951-51-7539-7 ; http://hdl.handle.net/10138/335002
URL:
http://hdl.handle.net/10138/335002
Content Provider:
HELDA – Helsingin yliopiston avoin julkaisuarkisto
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