摘要
通过分析传统中医药物间的影响关系和图结构数据节点间关系的共通性,将中医方剂学中处方的药物联系按规则转换为图结构数据,采用频繁闭图挖掘算法CloseGraph对图结构化的处方数据进行操作,得到图结构中代表具有特定功能的频繁闭图,再转换解释获得各中医方剂中对特定病症起决定疗效的核心药物组合及组合形式.结果表明,该方法可行、有效,成功地将图挖掘策略引入了中医方剂研究领域.
On the basis of analyzing the commonalities between the correlation of Traditional Chinese Medicines and the relationship of data nodes of the graph structure, we transfered the links of the drugs in a prescription into the graph structure data according to the rules. Dealing the structured prescription data with CloseGraph, an efficient frequent closed subgraph mining algorithm, we got frequent closed graph with a specific function of the graph structure. And then we obtained the core drug combinations and the forms of the combinations with a decisive effect on a specific disease for Traditional Chinese Medicines, providing a valuable scientific basis on figuring out the principles among disease-syndrome-formula. We successfully introduced the graph mining strategy into the field of Chinese Medicine research, providing a new idea and a more solid scientific theoretical foundation for both the prescription study and the future development of Traditional Chinese Medicines.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2012年第6期1223-1227,共5页
Journal of Jilin University:Science Edition
基金
国家自然科学基金青年基金(批准号:40801020)
关键词
图挖掘
频繁闭图
CloseGraph算法
中医方剂学
graph mining
frequent close-subgraph
CloseGraph algorithm
Traditional Chinese Medicine formula
