An empirical Bayes approach to inferring large-scale gene association networks

By Juliane Schäfer, Korbinian Strimmer. Bioinform., 2005

888 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bc85c2a7df8207afe6cd2cf1306ad957a296ee5a

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Citations in the corpus

Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks (2008, methodology): “…the inference of large-scale correlation networks (Butte et al., 2000) and of high-dimensional partial correlation graphs (Dobra et al., 2004; Schäfer and Strimmer, 2005a; Meinshausen and Bühlmann, 2006), for learning vector-autoregressive (Opgen-Rhein and Strimmer, 2007a) and state space…”
Inferring cellular networks – a review (2007, methodology): “They provide a strong measure of dependence and, correspondingly, only a weak criterion of independence [21].”
Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data (2011, methodology): “Partial correlations have recently been applied to biological data sets for the inference of association networks from mRNA expression data [ 12-15 ], and for the elucidation of relationships between genomic features in the human genome [16].”
BMC Bioinformatics (2005, positive): “Nowadays, explaining high-dimensional data with conditional independence structures is quite active especially in microarray data analysis [1, 22-24 ].”
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach (2007, background): “In [14], Schäfer and Strimmer develop a frequentist test fo r edge inclusion in graphical Gaussian models (GGMs) and provide a reasonable model for the null distribution of this test.”
Partial Correlation Estimation by Joint Sparse Regression Models (2008, methodology): “…methods have also been proposed for selecting the tuning parameter in the setting of covariance estimation, for example, the MSE based criterion in Schafer and Strimmer (2005), the likelihood based method in Huang et al. (2006), and the cross-validation and bootstrap methods in Li and Gui (2006).”
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008, methodology): “In spite of some evident limitations the bioinformatics community made important advances in this domain over the last few years [2,3].”
BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA (2011, background): “Gaussian Graphical Models (GGMs) [Lauritzen (1996); Sch¨afer and Strimmer (2005); Dobra et al. (2004)], a subclass of MNs, assume a multivariate normal distribution as the joint distribution of random variables.”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, positive): “In our original GGM model selection proposal (Schäfer and Strimmer, 2005a) we have relied on the FDR method to identify edges in the network.”
Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome (2017, background): “Partial correlations 52,53 between the centroid landmarks are then estimated to infer trajectories/dependencies between landmarks.”
WGCNA: an R package for weighted correlation network analysis (2008, background): “Co-expression networks have been found useful for describing the pairwise relationships among gene transcripts [2-9].”
NETWORK EXPLORATION VIA THE ADAPTIVE LASSO AND SCAD PENALTIES (2009, positive): “…following: Li and Gui (2006), who introduced a threshold gradient descent (TGD) regularization procedure for the sparse precision matrix estimation; Schäfer and Strimmer (2005), who estimated the correlation matrix via regularization with bootstrap variance reduction and used false discovery rate…”