在人工智能时代,传统中医药的网络药理学
Weibo Zhao1, Boyang Wang1, Shao Li1
1Institute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China.
Chinese herbal medicines
|November 28, 2024
概括
传统中医药网络药理学 (TCM-NP) 利用人工智能和大数据进行系统的TCM研究. 未来的进步需要更高质量的数据和先进的算法,以实现更精确,更有意义的发现.
科学领域:
- 综合医学是一个整体的医学.
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 传统中医药网络药理学 (TCM-NP) 通过整合信息科学,系统生物学,网络科学和药理学,为TCM研究提供了一种系统的方法.
- 人工智能 (AI) 和多omics技术的出现推动了TCM-NP进入一个新时代,使多式联络和高维大数据的整合成为可能.
研究的目的:
- 提供关于TCM-NP在AI时代的发展趋势和应用特征的视角.
- 突出AI和大数据在增强TCM-NP理论基础和技术能力方面的潜力.
- 确定TCM-NP研究中的挑战和创新领域.
主要方法:
- 对TCM-NP发展当前趋势的审查和综合.
- 在TCM研究中分析AI和多omics数据集成.
- 关于TCM-NP的未来方向的前景.
主要成果:
- 随着人工智能和大数据的不断发展,TCM-NP正在增强其系统研究能力.
- 在数据质量,研究完整性和实现深刻的科学发现方面仍然存在挑战.
- 进步需要更准确的算法和更高质量的,更大规模的数据集.
结论:
- 未来的TCM-NP依赖于开发卓越的算法,并利用改进的数据质量和数量.
- 进一步的创新对于在TCM研究中取得更精确和生物医学上更重要的结果至关重要.
- 人工智能集成为推进TCM-NP的理论和实践应用提供了重大机会.
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