DOI: 10.18129/B9.bioc.glmSparseNet  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see glmSparseNet.

Network Centrality Metrics for Elastic-Net Regularized Models

Bioconductor version: 3.17

glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".

Author: André Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb]

Maintainer: André Veríssimo <andre.verissimo at>

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HTML R Script Breast survival dataset using network from STRING DB
HTML R Script Example for Classification -- Breast Invasive Carcinoma
HTML R Script Example for Survival Data -- Breast Invasive Carcinoma
HTML R Script Example for Survival Data -- Prostate Adenocarcinoma
HTML R Script Example for Survival Data -- Skin Melanoma
HTML R Script Separate 2 groups in Cox regression
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biocViews Classification, DimensionReduction, GraphAndNetwork, Network, Regression, Software, StatisticalMethod, Survival
Version 1.18.0
In Bioconductor since BioC 3.8 (R-3.5) (5 years)
License GPL-3
Depends R (>= 4.1), Matrix, MultiAssayExperiment, glmnet
Imports SummarizedExperiment, biomaRt, futile.logger, futile.options, forcats, utils, dplyr, glue, readr, digest, httr, ggplot2, survminer, reshape2, stringr, parallel, methods
Suggests testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils
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