Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

By Anja Wille, Philip Zimmermann, E. Vranová, A. Fürholz, Oliver Laule, Stefan Bleuler, L. Hennig, A. Prelic, P. von Rohr, Lothar Thiele and 3 more. Genome Biology, 2004

417 citations recorded by the source corpus.

We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eba50a16826e34ee3041671120beabeaf3173802

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Citations in the corpus

Learning Multi-Level Task Groups in Multi-Task Learning (2015, neutral): “We report results on microarray data (Wille et al. 2004).”
Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network (2009, methodology): “Furthermore, in order to handle the case of a large number of nodes and a relatively small sample size, methods for estimating sparse GGMs have been developed [ 44 ].”
MissForest - non-parametric missing value imputation for mixed-type data (2011, background): “For more details on this data set see Wille et al. (2004).”
Early Steps in Isoprenoid Biosynthesis: Multilevel Regulation of the Supply of Common Precursors in Plant Cells (2006, methodology): “As a result, a close connection between the MVA and MEP pathways could not be detected, suggesting that the crosstalk on the transcriptional level may be restricted to single genes in both pathways (Wille et al., 2004).”
Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework (2014, methodology): “In this experiment, we report results on microarray data (Wille et al. 2004).”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, methodology): “We apply our methodology to microarray data described by Wille et al. (2004).”
Regularized rank-based estimation of high-dimensional nonparanormal graphical models (2012, methodology): “…noncausal structured approach to explore the complex systems consisting of Gaussian random variables, and it ﬁnds many interesting applications in areas such as gene expression genomics and macroeconomics determinants study [Friedman (2004), Wille et al. (2004), Dobra, Eicher and Lenkoski (2010)].”
The unicellular green alga Dunaliella salina Teod. as a model for abiotic stress tolerance: genetic advances and future perspectives (2011, background): “…other downstream plastidial isoprenoid pathways (Bouvier et al. 1998, Lois et al. 2000, Rodríguez-Concepción et al. 2003, Botella-Pavía et al. 2004, Wille et al. 2004, Rodríguez-Concepción 2010, Sun et al. 2010), identification of genes involved in the MEP pathway in Dunaliella represent the first…”
Metabolomics for functional genomics, systems biology, and biotechnology. (2010, methodology): “Coexpression analysis has also been used to predict genes or network modules involved in particular pathways, such as the isoprenoid pathway (218), brassinosteroid-regulated genes (115), the subtilase gene family (150), and genes for cold acclimation (76), with minimal or no functional verification ”
Network analysis of the MVA and MEP pathways for isoprenoid synthesis. (2013, methodology): “Several isoprenoid-pathway genes downstream of prenyl-PP also formed coexpression modules with genes from the MVA and MEP pathways (147), suggesting that concerted transcriptional regulation is not restricted to MVA- or MEP-pathway genes but has been expanded to connect downstream pathway genes to t”
Learning Tree Structure in Multi-Task Learning (2015, methodology): “2 Results on Microarray Data In this section, we conduct experiments on the microarray data [23],(5) a benchmark dataset for MTL.”
Multiple imputation for continuous variables using a Bayesian principal component analysis† (2014, background): “Thaliana.[36] This gene network includes p = 39 genes each with n = 118 gene expression profiles corresponding to different experimental conditions.”
Multi-level Lasso for Sparse Multi-task Regression (2012, methodology): “We apply our method to the analysis of gene expression data using a microarray dataset pertaining to isoprenoid biosynthesis in Arabidopsis thaliana (A. thaliana) provided by (Wille et al., 2004).”
Review of Causal Discovery Methods Based on Graphical Models (2019, background): “, 2011b), and to model the isoprenoid gene network in Arabidopsis thaliana (Wille et al., 2004).”
Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation (2016, background): “See, for instance, Wille et al. (2004) and Runge et al. (2014), for some recent applications in genomics and climate studies.”
An Overview on the Estimation of Large Covariance and Precision Matrices (2015, neutral): “Other related works are found in, e.g., Meinshausen and Bühlmann (2006a); Wille et al. (2004).”