Reference : Predictive and interpretable models via the stacked elastic net
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Engineering, computing & technology : Computer science
Human health sciences : Multidisciplinary, general & others
Computational Sciences; Systems Biomedicine
http://hdl.handle.net/10993/43221
Predictive and interpretable models via the stacked elastic net
English
Rauschenberger, Armin mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
van de Wiel, Mark []
In press
Bioinformatics
Oxford University Press
Yes (verified by ORBilu)
International
1367-4803
1367-4811
Oxford
United Kingdom
[en] machine learning ; bioinformatics ; stacked generalization ; stacking ; omics ; analysis ; prediction ; elastic net ; interpretability ; meta-learning ; R-package ; molecular data
[en] Motivation: Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques.
Results: Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability.
Availability and Implementation: The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet.
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Fonds National de la Recherche - FnR
FNR11651464 > PD-Strat (Dr. Glaab) > 01/07/2018 > 30/06/2021 > GLAAB Enrico
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/43221
10.1093/bioinformatics/btaa535
https://doi.org/10.1093/bioinformatics/btaa535
The original publication is available at https://doi.org/10.1093/bioinformatics/btaa535
FnR ; FNR11651464 > Enrico Glaab > PD-Strat > Multi-dimensional stratification of Parkinson’s disease patients for personalised interventions > 01/07/2018 > 30/06/2021 > 2018

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