时间序列数据的贝叶斯建模 (BayModTS) - - 一个公平的工作流程来处理稀疏和高度可变的数据
Sebastian Höpfl1, Mohamed Albadry2,3, Uta Dahmen2
1Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany.
我们开发了BayModTS,这是一个贝叶斯模型工作流程,用于稀疏和可变时间序列数据. 这种方法始终处理不确定性,使生物系统的强有力的分析和特定条件的动态的识别.
科学领域:
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
背景情况:
- 系统生物学中的定量动态建模面临着高可变性,低分辨率时间序列数据的挑战.
- 整合这些数据,同时始终处理不确定性对于准确的生物系统理解至关重要.
研究的目的:
- 介绍BayModTS (时间序列数据的贝叶斯建模),用于分析稀疏和可变时间序列的FAIR工作流.
- 证明工作流在将数据不确定性转移到模型预测和识别特定条件动态方面的能力.
主要方法:
- 开发了BayModTS,这是一个贝叶斯模型工作流程,用于处理和分析时间序列数据.
- 从数据转移到模型预测实现了一致的不确定性转移.
- 利用参数化模型来结合过程知识.
主要成果:
- BayModTS成功地处理了三个不同的肝脏数据集 (动物MRI,小鼠药理动力学,人类CT) 的稀疏和可变时间序列数据.
- 工作流程有效地转移了不确定性,并确定了条件之间的动态的可信差异.
- 在分析生物时间序列数据方面表现出强度和多功能性.
结论:
- BayModTS为分析具有挑战性的生物时间序列数据提供了强大的解决方案.
- 工作流程通过一致处理不确定性,提高了定量动态建模的可靠性.
- 通过改进数据集成和分析,促进对生物系统的更深入的理解.
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