人工智能辅助的文献探索创新的中医药配方
Meng-Chi Chung1, Li-Jen Su1,2,3,4,5, Chien-Lin Chen4,6,7
1Department of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan.
Frontiers in pharmacology
|April 8, 2024
概括
这项研究使用数据挖掘和生成模型来分析传统中医 (TCM) 文本,揭示了可持续性和生物多样性的新草本组合. 它将古老的智慧与现代的药物基因组学联系起来,提出创新的TCM配方.
科学领域:
- 计算生物学 计算生物学
- 药物基因组学 药物基因组学
- 民族植物学 民族植物学
背景情况:
- 传统中医 (TCM) 提供了丰富的潜在可持续和生物多样性友好的草药疗法的来源.
- 经典的中医文本,如普吉芳,包含了大量关于需要现代解释的草药配方的信息.
- 整合历史知识与当代科学方法对于验证和推进TCM实践至关重要.
研究的目的:
- 开发一种创新的数据挖掘方法来分析经典的TCM文本.
- 确定新的草药组合,并探索它们在可持续性和生物多样性保护方面的潜力.
- 使用生成模型将经典的TCM知识与现代的药物基因组数据相结合.
主要方法:
- 代关键词提取和关联规则挖掘在历史的中医文本 (Pu-Ji Fang).
- 在PubMed中进行基因草药关联搜索和遗传文献的统计验证.
- 基于长期短期记忆 (LSTM) 的生成语言模型的开发,用于新的TCM配方建议.
主要成果:
- 分析了来自Pu-Ji Fang. 426卷的16384个关键词组合.
- 鉴定了7664个PubMed交叉搜索项目的基因-草药关联.
- 基于古典文本分析成功开发了一种生成模型,用于提出基于古典文本分析的新植物成分.
结论:
- 经典的中医文本,如Pu-Ji Fang,强调了味道特征以及药用特性.
- 分析古代文本,现代遗传学和生成建模的综合方法增强了TCM的理解和应用.
- 这种方法促进了新的草药成分和TCM配方的快速生成,支持可持续性和生物多样性目标.
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