Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks
Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks
复制标题
DOI：
10.1007/978-1-4939-8882-2_2
复制
发表时间：
2019-01-01
期刊：
影响因子：
--
通讯作者：
Mukherjee, Sach
中科院分区：
文献类型：
--
作者：
Dondelinger, Frank;Mukherjee, Sach
在本章中，我们将回顾从时间过程数据中进行网络推理的问题，重点关注一类称为动态贝叶斯网络（DBN）的图形模型。我们讨论的DBN模型的基础上常微分方程的关系，并考虑扩展到非线性时间动力学。我们提供了一个介绍时变DBN模型，它允许随着时间的推移，网络结构和参数的变化。我们还讨论了网络推理的因果观点，包括由于缺失变量而可能出现的模型语义问题。我们提出了一个案例研究应用随时间变化的DBN的果蝇的生命周期的基因表达测量。最后，我们讨论了未来的前景，包括时变网络推理的单细胞基因表达数据的可能应用。
In this chapter, we review the problem of network inference from time-course data, focusing on a class of graphical models known as dynamic Bayesian networks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time. We also discuss causal perspectives on network inference, including issues around model semantics that can arise due to missing variables. We present a case study of applying time-varying DBNs to gene expression measurements over the life cycle of Drosophila melanogaster. We finish with a discussion of future perspectives, including possible applications of time-varying network inference to single-cell gene expression data.