Yoshida Ryo, Imoto Seiya, Higuchi Tomoyuki
Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 103-8569, Japan.
Proc IEEE Comput Syst Bioinform Conf. 2005:289-98. doi: 10.1109/csb.2005.32.
In gene network estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent gene network structure from time series gene expression data. Using our proposed method, the network structure between genes and its change points are automatically estimated. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.
在从时间序列微阵列数据估计基因网络时，诸如微分方程和动态贝叶斯网络等动态模型假定网络结构在所有时间点都是稳定的，而实际网络可能会根据时间、某些冲击的影响等改变其结构。如果数据背后的真实网络结构在某些点发生变化，那么通常的动态线性模型的拟合将无法估计基因网络的结构，并且我们无法从数据中获得有效的信息。为了解决这个问题，我们提出了一种具有马尔可夫切换的动态线性模型，用于从时间序列基因表达数据估计随时间变化的基因网络结构。使用我们提出的方法，可以自动估计基因之间的网络结构及其变化点。我们通过对酿酒酵母细胞周期时间序列数据的分析证明了所提出方法的有效性。
