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数据挖掘算法在中药方剂研究中的应用现状
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| 篇名： | 数据挖掘算法在中药方剂研究中的应用现状 |
| TITLE： | Application of data mining algorithms in research on traditional Chinese medicine formula |
| 摘要： | 近年来，数据挖掘算法在中药领域的科研中得到了广泛应用。采用数据挖掘算法可处理和分析中药方剂中的多层次数据，并对其作用机制提供合理解释。这一方法现已较好地应用于中医药的配伍规律和高频药组的挖掘中，提高了临床诊断、靶点筛选和新药研究的可靠性和准确性。本文对147篇中药方剂研究中运用数据挖掘算法的文献进行了整理与分析，结果表明，数据挖掘算法在中药方剂作用机制研究、中药方剂量效研究、挖掘核心药对/药组、挖掘“方-药-证”间的关系、发现新方剂和挖掘配伍规律这6个子领域中发挥了独特优势，尤以关联规则和聚类分析算法最具有代表性。 |
| ABSTRACT： | In recent years， data mining algorithms have been widely employed in scientific research within the field of traditional Chinese medicine （TCM）. The data mining algorithms are used to effectively handle and analyze the complex data in TCM formulas， providing a rational explanation for the mechanism of action. This method has proven particularly useful in uncovering patterns of compatibility and frequent combinations of herbs in TCM， thereby enhancing the reliability and accuracy of clinical diagnosis， target screening， and the study of new drugs. This paper reviews and analyzes 147 papers on TCM formula research that utilize data mining algorithms. The results indicate that data mining algorithms play a unique advantage in six sub- areas， including the study on the mechanism of action in TCM formula， the dose-efficacy of TCM formulas， the identification of core drugs pairs/groups， mining the relationships among “formulas-drug-symptom”， the discovery of new formulas， and mining the compatibility law. Notably， association rules and clustering algorithms are the most representative. |
| 期刊： | 2024年第35卷第01期 |
| 作者： | 李蕙质；周小玲；杨玉杰；章新友 |
| AUTHORS： | LI Huizhi，ZHOU Xiaoling，YANG Yujie，ZHANG Xinyou |
| 关键字： | 数据挖掘算法；中药方剂；文献计量法；应用 |
| KEYWORDS： | data mining algorithms； traditional Chinese medicine formula； bibliometrics analysis； application |
| 阅读数： | 71 次 |
| 本月下载数： | 14 次 |
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