Jia Bin, Wang Xiaodong
Intelligent Fusion Technology, Germantown, Inc., MD 20876, USA.
Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
EURASIP J Bioinform Syst Biol. 2014;2014(1):5. doi: 10.1186/1687-4153-2014-5. Epub 2014 Apr 3.
Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm.
动态系统中的参数估计在包括系统生物学在内的各个学科中都有应用。著名的期望最大化（EM）算法是一种常用方法，已被广泛用于解决系统辨识和参数估计问题。然而，传统的EM算法无法利用稀疏性。另一方面，在基因调控网络推断问题中，待估计的参数通常呈现稀疏结构。本文提出了一种基于最大后验（MAP）估计且能纳入稀疏先验的用于非线性动态系统稀疏参数估计的正则化期望最大化（rEM）算法。期望步骤涉及前向高斯近似滤波和后向高斯近似平滑。最大化步骤采用重新加权迭代阈值法。然后将所提出的算法应用于基因调控网络推断。基于合成数据和真实数据的结果表明了所提算法的有效性。
