TY - JOUR
T1 - Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis
AU - Lu, Tao
N1 - Publisher Copyright:
© 2016 Taylor & Francis.
PY - 2016/7/3
Y1 - 2016/7/3
N2 - 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.
AB - 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.
KW - Group SCAD
KW - mixed-effects models
KW - time course microarray data
KW - variable selection
UR - http://www.scopus.com/inward/record.url?scp=84954469395&partnerID=8YFLogxK
U2 - 10.1080/10543406.2015.1052496
DO - 10.1080/10543406.2015.1052496
M3 - Article
C2 - 26098537
AN - SCOPUS:84954469395
SN - 1054-3406
VL - 26
SP - 712
EP - 724
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 4
ER -