本研究結合古代中醫知識與現代計算方法，運用資料與文本探勘、 Apriori 演算法 與網絡分析，挖掘《普濟方》等文獻中的草藥組合與應用模式。透過關鍵字提取、命名 實體識別及 PubMed 基因資料交叉分析，探索中醫在抗菌與糖尿病等疾病治療的潛力。 研究顯示：（ 1）歷史方劑強調藥效與風味；（ 2）地榆-澤瀉、苦參-生薑等組合具抗菌活 性；（ 3）「消渴門」草藥與代謝途徑高度相關。另應用資料探勘技術提出潛在新配方， 結合分子預測工具分析其化學成分與活性，展現中醫與現代生物資訊整合之可能。研究 提供新穎資料驅動框架，助攻個人化醫療與永續藥物發現。;This study explores the potential of Traditional Chinese Medicine (TCM) through computational methods, integrating ancient wisdom with modern drug discovery and sustainability advancements. TCM′s historical literature provides a valuable resource for analyzing classical texts like the Pu-Ji Fang through data mining, text mining, and network analysis. The main objective is to explore new therapeutic drug candidates, analyze herb usage patterns, and generate novel herbal formulations. One aspect investigates TCM’s role in combating microbial infections by applying the Apriori algorithm and case studies to explore traditional remedies, while another examines its potential for treating widespread diseases like diabetes. Sophisticated methodologies included a novel iterative keyword extraction method and association rules to identify key herb pairs from historical TCM texts, which studies cross-referenced with pharmacogenomic data from PubMed. Named Entity Recognition (NER) and external knowledge graphs analyzed herbal formulas related to specific organs and diseases, such as "XiaoKe" (diabetes). The Apriori algorithm identified frequent herb combinations, while tools like DAVID analyzed herb-to-gene networks, revealing biological functions and therapeutic potentials. Apriori-based learning reveals novel herbal formulations from frequent textual patterns. An antimicrobial molecular prediction tool analyzed the chemical composition of these herbs to identify antimicrobial effects. The integrated methods revealed insights into TCM: (1) Analysis of Pu-Ji Fang indicated that historical prescriptions emphasized medicinal value and flavor; (2) Herb combinations like DiYu → ZeXie and KuShen → ShengJiang demonstrated potential antimicrobial activity; (3) Network analysis of "XiaoKe" herbs highlighted associations with metabolic pathways, suggesting roles in regulation and metabolism. Additionally, the Apriori algorithm rapidly explored novel herbal combinations in ancient literature. Extensive data, including PubMed gene-herb entries, highlighted the potential of linking historical herbal knowledge with modern genetics. In conclusion, this study underscores the value of combining ancient TCM with modern science. Techniques such as data mining and network analysis deepen TCM insights and support new drug discovery. These methods may aid in personalized medicine and the development of sustainable treatments for infections and metabolic diseases.
