|Title||Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Xue, Hongqi, Shuang Wu, Yichao Wu, Juan C. Ramirez Idarraga, and Hulin Wu|
|Date Published||2018 Jul 30|
|Keywords||Algorithms, Computer Simulation, Gene Regulatory Networks, Humans, Mathematics, Models, Statistical, Nonlinear Dynamics|
Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae.
|Alternate Journal||Stat Med|
|Original Publication||Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.|
|PubMed Central ID||PMC6940146|
|Grant List||R01 AI087135 / AI / NIAID NIH HHS / United States |
HHSN266200700008C / AI / NIAID NIH HHS / United States
HHSN272201000055C / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 AI078498 / AI / NIAID NIH HHS / United States
Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.