A computer method for validating traditional Chinese medicine herbal prescriptions.

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions.

[1]  Professor Dr. Weici Tang,et al.  Chinese Drugs of Plant Origin , 1992, Springer Berlin Heidelberg.

[2]  Gerhard Eisenbrand,et al.  Chinese Drugs of Plant Origin: Chemistry, Pharmacology, and Use in Traditional and Modern Medicine , 1992 .

[3]  K. Chan,et al.  Progress in traditional Chinese medicine. , 1995, Trends in pharmacological sciences.

[4]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[5]  R Yuan,et al.  Traditional Chinese medicine: an approach to scientific proof and clinical validation. , 2000, Pharmacology & therapeutics.

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Juei-Tang Cheng,et al.  Review: Drug Therapy in Chinese Traditional Medicine , 2000, Journal of clinical pharmacology.

[9]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  C Y Ung,et al.  Computer automated prediction of potential therapeutic and toxicity protein targets of bioactive compounds from Chinese medicinal plants. , 2002, The American journal of Chinese medicine.

[11]  Thomas Hofmann,et al.  Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms , 2002, J. Comput. Biol..

[12]  C. Ung,et al.  Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients? , 2003, Natural product reports.

[13]  X. Chen,et al.  SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence , 2003, Nucleic Acids Res..

[14]  K. Shimamoto,et al.  Chinese medicine, Jiang-Tang-Ke-Li, improves insulin resistance by modulating muscle fiber composition and muscle tumor necrosis factor-alpha in fructose-fed rats. , 2003, Hypertension research : official journal of the Japanese Society of Hypertension.

[15]  A. Dasgupta,et al.  Study on mechanism of action of Chinese medicine Chan Su: dose-dependent biphasic production of nitric oxide in trophoblastic BeWo cells. , 2003, Clinica chimica acta; international journal of clinical chemistry.

[16]  Y Xue,et al.  Prediction of torsade-causing potential of drugs by support vector machine approach. , 2004, Toxicological sciences : an official journal of the Society of Toxicology.

[17]  M. Lazar.  East meets West: an herbal tea finds a receptor. , 2004, The Journal of clinical investigation.

[18]  Xin Chen,et al.  Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents , 2004, J. Chem. Inf. Model..

[19]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[20]  J. Moss,et al.  Herbal medicines and perioperative care. , 2001, Anesthesiology.