Lèbre Sophie, Dondelinger Frank, Husmeier Dirk
Université de Strasbourg, LSIIT - UMR 7005, Strasbourg, France.
Methods Mol Biol. 2012;802:199-213. doi: 10.1007/978-1-61779-400-1_13.
Dynamic Bayesian networks (DBNs) have received increasing attention from the computational biology community as models of gene regulatory networks. However, conventional DBNs are based on the homogeneous Markov assumption and cannot deal with inhomogeneity and nonstationarity in temporal processes. The present chapter provides a detailed discussion of how the homogeneity assumption can be relaxed. The improved method is evaluated on simulated data, where the network structure is allowed to change with time, and on gene expression time series during morphogenesis in Drosophila melanogaster.
动态贝叶斯网络（DBNs）作为基因调控网络的模型，已受到计算生物学界越来越多的关注。然而，传统的DBNs基于齐次马尔可夫假设，无法处理时间过程中的非齐次性和非平稳性。本章详细讨论了如何放宽齐次性假设。在模拟数据（允许网络结构随时间变化）以及黑腹果蝇形态发生过程中的基因表达时间序列上，对改进后的方法进行了评估。
