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Activity Number: 548
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #316274
Title: Network Reconstruction for Ordinary Differential Equations
Author(s): Shizhe Chen* and Daniela Witten and Ali Shojaie
Companies: University of Washington and University of Washington and University of Washington
Keywords: ODE ; Group lasso ; High dimensionality
Abstract:

We consider the task of learning dynamic systems from high-dimensional time-course data, for instance, estimation of the gene regulatory network from gene expression data. We model the dynamical system non-parametrically as additive ordinary differential equations. Common methods for parameter estimation in ordinary differential equations involves the estimation of derivatives from noisy observations, which has been shown to be challenging and inefficient. We propose a novel approach that does not involves derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and demonstrate empirical improvement of network recovery with numerical experiments.


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