title: Sparse time series chain graphical models for reconstructing genetic networks.
abstract: We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of contemporaneous and dynamic interactions by efficiently combining Gaussian graphical models and Bayesian dynamic networks. We use penalized likelihood inference with a smoothly clipped absolute deviation penalty to explore the relationships among the observed time course gene expressions. The method is illustrated on simulated data and on real data examples from Arabidopsis thaliana and mammary gland time course microarray gene expressions.
authors: Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Nijenborgh 9, The Netherlands. f.abegaz.yazew@rug.nl
authors: Fentaw
authors: F
authors: Abegaz
authors: Ernst
authors: E
authors: Wit
mesh_terms: D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D001499:Bayes Theorem; D056808:Biostatistics; D003198:Computer Simulation; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D016013:Likelihood Functions; D008321:Mammary Glands, Animal / Q000378:metabolism; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical
keywords: Chain graphical mode; Dynamic network; Gene expression; High-dimensional data; L1 penalty; Model selection; Penalized likelihood; SCAD penalty; Vector autoregressive model