Reference : Predicting correlated outcomes from molecular data

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Biotechnology Life sciences : Multidisciplinary, general & others Engineering, computing & technology : Computer science Human health sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/47788

Title :	Predicting correlated outcomes from molecular data
Language :	English
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 >]
Publication date :	2021
Journal title :	Bioinformatics
Volume :	37
Issue/season :	21
Pages :	3889–3895
Peer reviewed :	Yes
Audience :	International
Keywords :	[en] 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 centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	FNR11651464 > PD-Strat (Dr. Glaab) > 01/07/2018 > 30/06/2021 > GLAAB Enrico
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/47788
DOI :	10.1093/bioinformatics/btab576
Other URL :	https://doi.org/10.1093/bioinformatics/btab576
Mentions required by the publisher for OA :	The original publication is available at https://doi.org/10.1093/bioinformatics/btab576
Framework Programme / European Project :	H2020 ; 874825 - Personalized Medicine Trials (PERMIT)
FnR project :	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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	Fulltext file(s): File Commentary Version Size Access Open access joinet.pdf Author preprint 420.33 kB View/Open

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