glmSparseNet: Network Centrality Metrics for Elastic-Net Regularized Models

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".

Package details

AuthorAndré Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb]
Bioconductor views Classification DimensionReduction GraphAndNetwork Network Regression Software StatisticalMethod Survival
MaintainerAndré Veríssimo <>
LicenseGPL (>=3)
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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glmSparseNet documentation built on April 14, 2021, 6 p.m.