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eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models.

Julián CandiaJohn S Tsang
Published in: BMC bioinformatics (2019)
This package presents a framework and software for exploratory data analysis and visualization. By making regularized GLMs more accessible and interpretable, eNetXplorer guides the process to generate hypotheses based on features significantly associated with biological phenotypes of interest, e.g. to identify biomarkers for therapeutic responsiveness. eNetXplorer is also generally applicable to any research area that may benefit from predictive modeling and feature identification using regularized GLMs. The package is available under GPL-3 license at the CRAN repository, https://CRAN.R-project.org/package=eNetXplorer .
Keyphrases
  • data analysis
  • machine learning
  • quality improvement
  • high resolution
  • neural network