[en] Motivation: Multivariate (multi-target) regression has the potential to outperform univariate (single-target) regression at predicting correlated outcomes, which frequently occur in biomedical and clinical research. Here we implement multivariate lasso and ridge regression using stacked generalisation. Results: Our flexible approach leads to predictive and interpretable models in high-dimensional settings, with a single estimate for each input-output effect. In the simulation, we compare the predictive performance of several state-of-the-art methods for multivariate regression. In the application, we use clinical and genomic data to predict multiple motor and non-motor symptoms in Parkinson’s disease patients. We conclude that stacked multivariate regression, with our adaptations, is a competitive method for predicting correlated outcomes. Availability and Implementation: The R package joinet is available on GitHub (https://github.com/rauschenberger/joinet) and CRAN (https://CRAN.R-project.org/package=joinet).
Centre de recherche :
- Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Disciplines :
Biotechnologie Sciences de la santé humaine: Multidisciplinaire, généralités & autres Sciences informatiques Sciences du vivant: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
RAUSCHENBERGER, Armin ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science
GLAAB, Enrico ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Predicting correlated outcomes from molecular data
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