{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:10:26Z","timestamp":1775027426711,"version":"3.50.1"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"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":["I1R-BIC-PFN-15NCER"],"award-info":[{"award-number":["I1R-BIC-PFN-15NCER"]}]},{"name":"ERA-Net ERACoSysMed JTC-2","award":["INTER\/11651464"],"award-info":[{"award-number":["INTER\/11651464"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularization. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package starnet is available on GitHub (https:\/\/github.com\/rauschenberger\/starnet) and CRAN (https:\/\/CRAN.R-project.org\/package=starnet).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa535","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T11:28:45Z","timestamp":1589801325000},"page":"2012-2016","source":"Crossref","is-referenced-by-count":23,"title":["Predictive and interpretable models via the stacked elastic net"],"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"},{"name":"Department of Epidemiology and Data Science , Amsterdam UMC, 1081 HV Amsterdam, The Netherlands"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4780-8472","authenticated-orcid":false,"given":"Mark A","family":"van de Wiel","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Data Science , Amsterdam UMC, 1081 HV Amsterdam, The Netherlands"},{"name":"MRC Biostatistics Unit, University of Cambridge , CB2 0SR Cambridge, UK"}]}],"member":"286","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"2024040222425693100_btaa535-B1","doi-asserted-by":"crossref","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","article-title":"Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays","volume":"96","author":"Alon","year":"1999","journal-title":"Proc. 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