Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13150
Title: In silico approaches in the study of traditional Chinese herbal medicine
Authors: UNG CHOONG YONG
Keywords: TCM, herbal medicine, machine learning, SVM, docking, synergy
Issue Date: 4-Jun-2008
Source: UNG CHOONG YONG (2008-06-04). In silico approaches in the study of traditional Chinese herbal medicine. ScholarBank@NUS Repository.
Abstract: Recent development of Systems Biology in this b omicsb era reinforced the therapeutic strategy of considering human systems as a whole. Multi-herb prescriptions have been routinely used in TCM formulated by using TCM-defined herbal properties (TCM-HPs) where the scientific basis is unclear. Machine learning methods (MLMs) are used to explore the scientific basis of TCM prescription formulation. The studies reveal that MLMs are capable of classifying TCM prescriptions and herb pairs from those of random herb combinations showing that there is hidden scientific rule in the formulation of TCM prescriptions. Besides, a structural approach using inverse docking method (INVDOCK) is used to identify putative metastatic-related targets of Rhubarb anthraquinones from a protein structure database. The results implicate additive or synergistic effects of Rhubarb anthraquinones in anti-metastasis when used in combinations. In addition, current study of herbal synergism using literature-based approach reveals multiple mechanisms that involve either similar or distinct molecular targets as well as signaling pathways. In general, current in silico approaches used in this study covered both traditional and molecular aspects of TCM from top-down and bottom-up directions.
URI: http://scholarbank.nus.edu.sg/handle/10635/13150
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
In Silico Approaches in the Study of Traditional Chinese Herbal Medicine.pdf2.36 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

442
checked on Dec 11, 2017

Download(s)

864
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.