Article (Scientific journals)
Predicting correlated outcomes from molecular data
RAUSCHENBERGER, Armin; GLAAB, Enrico
2021In Bioinformatics, 37 (21), p. 3889–3895
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Keywords :
machine learning; prediction; classification; correlated outcomes; model interpretability; multivariate; stacking; ensemble learning; software; Parkinson's disease; lasso; ridge regression
Abstract :
[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).
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Disciplines :
Biotechnology
Human health sciences: Multidisciplinary, general & others
Computer science
Life sciences: Multidisciplinary, general & others
Author, co-author :
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
External co-authors :
no
Language :
English
Title :
Predicting correlated outcomes from molecular data
Publication date :
06 August 2021
Journal title :
Bioinformatics
ISSN :
1367-4803
eISSN :
1367-4811
Volume :
37
Issue :
21
Pages :
3889–3895
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Systems Biomedicine
European Projects :
H2020 - 874825 - PERMIT - PERsonalised MedicIne Trials
FnR Project :
FNR11651464 - Multi-dimensional Stratification Of Parkinson'S Disease Patients For Personalised Interventions, 2017 (01/07/2018-30/06/2021) - Enrico Glaab
Name of the research project :
FNR11651464 > PD-Strat (Dr. Glaab) > 01/07/2018 > 30/06/2021 > GLAAB Enrico
Funders :
FNR - Fonds National de la Recherche
CE - Commission Européenne
Union Européenne
Available on ORBilu :
since 05 August 2021

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