非线性动态系统稀疏参数估计的正则化EM算法及其在基因调控网络推理中的应用。

Bin Jia, Xiaodong Wang
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引用次数: 6

摘要

动态系统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。期望最大化(EM)算法是一种流行的方法,已广泛用于解决系统辨识和参数估计问题。然而,传统的电磁算法不能充分利用稀疏性。另一方面,在基因调控网络推理问题中,待估计的参数往往呈现稀疏结构。提出了一种基于最大后验(MAP)估计并结合稀疏先验的非线性动态系统稀疏参数估计正则化期望最大化算法。期望步包括前向高斯逼近滤波和后向高斯逼近平滑。最大化步骤采用重新加权迭代阈值法。将该算法应用于基因调控网络的推理。基于合成数据和实际数据的结果表明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.

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.

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