聚类血度-时间曲线:无监督学习在药物基因组学中的应用
Jackson P Lautier1, Stella Grosser2, Jessica Kim2
1Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts, USA.
Journal of biopharmaceutical statistics
|June 18, 2024
概括
药代动力学 (PK) 曲线的机器学习聚类有效地识别了类似的药物配置文件. 这种方法验证了现有的发现,并揭示了在没有遗传数据的情况下药物行为中的新模式.
科学领域:
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 提供了改善药物开发的潜力.
- 药物动力学 (PK) 曲线的无监督聚类是一个研究不足的ML应用.
- PK曲线是时间序列数据,需要适当的不相似度.
研究的目的:
- 调查 PK 曲线的无监督聚类的有效性.
- 为了确定适合PK曲线集群的不相似度.
- 在现实世界的案例研究中展示PK曲线聚类的实用性.
主要方法:
- 在 PK 曲线上应用了无监督的集群算法.
- 评估了各种时间序列不相似度,包括欧几里德距离,动态时间扭曲,Fréchet和相关性.
- 从药物基因组学研究中使用250个PK曲线进行了一项案例研究.
主要成果:
- 发现欧几里德距离是聚类PK曲线的最合适的测量方法.
- 聚类成功地确定了类似的PK曲线形状,并揭示了群体内的模式.
- 无监督的ML集群是从先前的药物基因组研究中独立验证的结论.
- 聚类提供了从人口水平的PK指标中看不到的洞察力.
结论:
- 无监督的PK曲线集群是制药研究中的一个有价值的工具.
- 建议在聚类PK曲线时使用欧几里德距离.
- 基于ML的PK曲线聚类可以补充传统的药物基因组分析,并揭示隐藏的模式.
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