Research on Component Law of Chinese Patent Medicine for Anti-influenza and Development of New Recipes for Anti-influenza by Unsupervised Data Mining Methods

• Published in: Journal of traditional Chinese medicine Vol. 30; no. 4; pp. 288 - 293
• Main Author: 唐仕欢 陈建新 李耿 吴宏伟 陈畅 张娜 高娜 杨洪军 黄璐琦
• Format: Journal Article
• Language: English
• Published: China 01.12.2010
• Subjects: Antiviral Agents - analysis; Antiviral Agents - therapeutic use; Chemistry, Pharmaceutical; Data Mining; Drugs, Chinese Herbal - analysis; Drugs, Chinese Herbal - therapeutic use; Humans; Influenza A Virus, H1N1 Subtype - drug effects; Influenza A Virus, H1N1 Subtype - physiology; Influenza, Human - drug therapy; Influenza, Human - virology; Nonprescription Drugs - analysis; Nonprescription Drugs - therapeutic use; 中国专利; 中成药; 基于构件; 挖掘方法; 新处方; 新配方
• ISSN: 0255-2922
• DOI: 10.1016/S0254-6272(10)60058-1

Objective：To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods： Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results： Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion： Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.