Network‐Based Penalized Regression With Application to Genomic Data

DOI10.1111/biom.12035zbMath1429.62294OpenAlexW1484418241WikidataQ30652883 ScholiaQ30652883MaRDI QIDQ2861942

Xiaotong Shen, Wei Pan, Sunkyung Kim

Publication date: 13 November 2013

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4007772

zbMATH Keywords

nonconvex minimizationgene expressionpenaltynetworks analysistruncated Lasso penalty

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (8)

Graph structured sparse subset selection ⋮ Graph-based sparse linear discriminant analysis for high-dimensional classification ⋮ Prediction models with graph kernel regularization for network data ⋮ A general framework for penalized mixed-effects multitask learning with applications on DNA methylation surrogate biomarkers creation ⋮ DC programming and DCA: thirty years of developments ⋮ Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior ⋮ High-dimensional generalized linear models incorporating graphical structure among predictors ⋮ What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

Uses Software

• CVX
• KEGG

Cites Work

• Unnamed Item
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• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR

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