Network Reconstruction From High-Dimensional Ordinary Differential Equations

• 成果类型: Article
• 署名作者: Chen, Shizhe; Shojaie, Ali; Witten, Daniela M.
• 署名单位: University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1229197
• 发表日期: 2017
• 页码: 1697-1707
• 关键词: parameter-estimation; dynamic-models; selection; regression; systems; error; noisy; Lasso; odes

摘要

We consider the task of learning a dynamical systemfromhigh-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. Wemodel the dynamical system nonparametrically as a systemof additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposedmethod can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.