Wang Z, Yang F, Ho D W C, Swift S, Tucker A, Liu X
Department of Information Systems and Computing, Brunel University, Uxbridge, UK.
IEEE Trans Nanobioscience. 2008 Mar;7(1):44-55. doi: 10.1109/TNB.2008.2000149.
In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarrary gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed.
在本文中，期望最大化（EM）算法被应用于从基因时间序列数据对基因调控网络进行建模。基因调控网络被视为一个随机动态模型，它由来自微阵列的有噪声基因测量值以及基因调控一阶自回归（AR）随机动态过程组成。通过使用EM算法，可以同时识别模型参数和基因表达水平的实际值。此外，该算法能够以有效的方式处理稀疏参数识别和噪声数据。还表明EM算法可以处理具有大量变量但少量观测值的微阵列基因表达数据。构建了四个真实世界基因表达数据集的基因表达随机动态模型，以证明所引入算法的优势。提出了几个指标来评估推断的基因调控网络模型，并讨论了相关的生物学特性。
