Reference : Predicting correlated outcomes from molecular data
Scientific journals : Article
Life sciences : Biotechnology
Life sciences : Multidisciplinary, general & others
Engineering, computing & technology : Computer science
Human health sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/47788
Predicting correlated outcomes from molecular data
English
Rauschenberger, Armin mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
In press
Bioinformatics
Oxford University Press
Yes (verified by ORBilu)
International
1367-4803
1367-4811
Oxford
United Kingdom
[en] machine learning ; prediction ; classification ; correlated outcomes ; model interpretability ; multivariate ; stacking ; ensemble learning ; software ; Parkinson's disease ; lasso ; ridge regression
[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).
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/10993/47788
10.1093/bioinformatics/btab576
https://doi.org/10.1093/bioinformatics/btab576
The original publication is available at https://doi.org/10.1093/bioinformatics/btab576
H2020 ; 874825 - Personalized Medicine Trials (PERMIT)
FnR ; FNR11651464 > Enrico Glaab > PD-Strat > Multi-dimensional Stratification Of Parkinson’S Disease Patients For Personalised Interventions > 01/07/2018 > 30/06/2021 > 2017

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