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Showing 1 to 11 of 11 for “"Graphical Lasso"”.
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Network inference via clustered fused graphical lasso
Embargo set by: Seth Robbins for item 107307 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system
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Inference of high-dimensional linear models with time-varying coefficients
… nonparametric kernel smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance decomposition to address non-stationarity in the model. A hypothesis testing setup with familywise error control is presented alongside synthetic data and a real application to …
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… prototypical dynamic trajectory. In the Bayesian Graphical LASSO, the inverse covariance matrix of the data distribution is assumed to be sparse, inducing a sparsely connected Gaussian graph. In the nonparametric Mixture of Factor Analyzers, the covariance matrices in the Gaussian Mixture Model …
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Joint Gaussian Graphical Model for multi-class and multi-level data
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks …
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Methods for the estimation and application of biological networks
… in disease. The first method, the "Joint Graphical Lasso," is an extension of existing network estimation methods to datasets with multiple classes of observations, for example cancer and healthy cells. We describe a convex penalized likelihood equation whose solution has desirable …
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Prediction of CYP3A4 metabolic activity from whole genome RNA-seq data with feature selection machine learning methods
… high-dimensionality of the dataset, we applied lasso and elastic net, two feature selection machine learning methods, for prediction and graphical lasso was used for constructing gene network graphs. A simulation study was performed to assess the performance of the prediction algorithms and to …
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Interdisciplinary Studies of Complex Network and Machine Learning and Its Applications
… inference, we introduce correlation matrix, graphical Lasso, network clustering and identify the influencer in the network. For variable inference, we also introduce from Bayesian network, to Random Markov Field and Ising Model, Boltzmann and Restricted Boltzmann machine and the algorithm of …
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Joint Network Modeling of Omics Data for Understanding Complex Diseases
… selection procedure for the widely used joint graphical lasso that maintains high performance for high-dimensional data, and I demonstrate the potential of the method on proteomic data from a pan-cancer study. While popular for network modelling due to desirable properties, Bayesian estimators …
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Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii
… networks under multiple conditions: the Joint Graphical Lasso (JGL), a shrinkage based Gaussian graphical model. We apply this method to two data sets: one, a publicly available set of microarray experiments perturbing the gram-positive bacteria Bacillus subtilis under multiple experimental …
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Statistical Methods for Genetic Pathway-Based Data Analysis
… the other is to propose a multilevel Gaussian graphical model for exploring both pathway and gene level network structures. For the first problem, we develop a semiparametric model via a Bayesian hierarchical framework. We model the pathway effect nonparametrically into a zero inflated Poisson …
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Theory-based Explorations of Associations between Human Brain Structure and Intelligence from Childhood to Early Adulthood
… used network analytic methods (specifically graphical LASSO) to simultaneously model brain-behaviour relationships essential for general intelligence in a large (behavioural, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165), developmental (ages 5 – 18 years) cohort of …