{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:09:05Z","timestamp":1768878545468,"version":"3.49.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Luxembourg National Research Fund"},{"name":"National Centre for Excellence in Research on Parkinson\u2019s disease","award":["11651464"],"award-info":[{"award-number":["11651464"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["2020-314"],"award-info":[{"award-number":["2020-314"]}]},{"DOI":"10.13039\/100000864","name":"Michael J. Fox Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000864","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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 generalization.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our flexible approach leads to predictive and interpretable models in high-dimensional settings, with a single estimate for each input\u2013output 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\u2019s disease patients. We conclude that stacked multivariate regression, with our adaptations, is a competitive method for predicting correlated outcomes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package joinet is available on GitHub (https:\/\/github.com\/rauschenberger\/joinet) and cran (https:\/\/cran.r-project.org\/package=joinet).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab576","type":"journal-article","created":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T11:26:39Z","timestamp":1628162799000},"page":"3889-3895","source":"Crossref","is-referenced-by-count":11,"title":["Predicting correlated outcomes from molecular data"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6498-4801","authenticated-orcid":false,"given":"Armin","family":"Rauschenberger","sequence":"first","affiliation":[{"name":"Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg , 4362 Esch-sur-Alzette, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3977-7469","authenticated-orcid":false,"given":"Enrico","family":"Glaab","sequence":"additional","affiliation":[{"name":"Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg , 4362 Esch-sur-Alzette, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"2023051608252251900_btab576-B1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jclinepi.2007.03.002","article-title":"Polytomous logistic regression analysis could be applied more often in diagnostic research","volume":"61","author":"Biesheuvel","year":"2008","journal-title":"J. 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