title: Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.
abstract: The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.
authors.affiliation: a Department of Epidemiology and Biostatistics , State University of New York , Rensselaer , New York , USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Lu
mesh_terms: D015870:Gene Expression; D053263:Gene Regulatory Networks; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D018709:Statistics, Nonparametric
keywords: Group SCAD; mixed-effects models; time course microarray data; variable selection