Predicting correlated outcomes from molecular data

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 :

Language :

English

Title :

Predicting correlated outcomes from molecular data

Publication date :

06 August 2021

Journal title :

Bioinformatics

Volume :

Issue :

Pages :

3889–3895

Peer reviewed :

Peer reviewed

Focus Area :

Systems Biomedicine

Additional URL :

https://doi.org/10.1093/bioinformatics/btab576

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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