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 ![]() | |
Glaab, Enrico ![]() | |
2021 | |
Bioinformatics | |
37 | |
21 | |
3889–3895 | |
Yes | |
International | |
[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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