Sparse graphical models for exploring gene expression data

By A. Dobra, Chris Hans, Beatrix Jones, Joseph R. Nevins, Guang Yao, Michael West. 2004

492 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

An empirical Bayes approach to inferring large-scale gene association networks (2005, unknown): “…for choosing a GGM network consistent with the data is to test each of the E = G ( G − 1) / 2 potential edges individually for presence in the ﬁnal network, i.e. whether the corresponding partial correlation coefﬁcient is signiﬁcantly different from zero (Whittaker, 1990; Drton and Perlman, 2004).”
BMC Bioinformatics (2005, unknown): “Nowadays, explaining high-dimensional data with conditional independence structures is quite active especially in microarray data analysis [1,22-24].”
First-Order Methods for Sparse Covariance Selection (2006, unknown): “This has applications in speech recognition (see [2, 3]) or gene networks analysis (see [9, 8] for example).”
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.”
Bayesian variable selection for high dimensional generalized linear models : Convergence rates of the fitted densities (2007, unknown): “Other work applying BVS in the GLM framework includes, for example, Clyde and DeSimone-Sasinowska [3], Nott and Leonte [21] and Wang and George [24].”
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis-Hastings (2021, methodology): “…work on dependency networks (Heckerman et al., 2000) proposed a similar scheme of training the conditionals independently with separate regressions over the latent variables and the inconsistency of such conditionals is well documented (Gelman and Raghunathan, 2001; Dobra et al., 2004; Lowd, 2012).”
Learning Gaussian Graphical Models with Observed or Latent FVSs (2013, background): “Beyond thin-junction trees, the family of sparse GGMs is also widely studied [9, 10].”
Estimating brain functional connectivity with sparse multivariate autoregression (2005, unknown): “A considerable improvement may be achieved by stochastic search variable selection (SSVS), which relies on Markov chain–Monte Carlo (MCMC) exploration of possible sparse networks (Dobra et al .”
Inferring cellular networks – a review (2007, unknown): “When learning dependency networks, a variety of sparse classification/regression techniques may be used to estimate the local distributions, including linear models with an L1-penalty on model parameters [20,26], classification trees [25,27], or sparse Bayesian regression [19,28].”
Temporal causal modeling with graphical granger methods (2007, unknown): “The use of MCMC limits the applicability of this method to relatively small numbers of features p, as analyzed in [4].”
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks (2008, background): “…been proposed to enable 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,…”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, unknown): “Fitting this mixture density allows κ, η 0 and even f A to be determined – for an algorithm to infer the latter see Efron (2004, 2005b).”