{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T13:36:36Z","timestamp":1771853796458,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Luxembourg National Research Fund"},{"name":"Clinnova\u2014Federating Digital Medicine in Europe","award":["ncer\/23\/16695277"],"award-info":[{"award-number":["ncer\/23\/16695277"]}]},{"name":"Luxembourg Institute of Health"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>Here, we propose a simple two-stage procedure for sharing information between related high-dimensional prediction or classification problems. In both stages, we perform sparse regression separately for each problem. While this is done without prior information in the first stage, we use the coefficients from the first stage as prior information for the second stage. Specifically, we designed feature-specific and sign-specific adaptive weights to share information on feature selection, effect directions, and effect sizes between different problems.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The proposed approach is applicable to multi-task learning as well as transfer learning. It provides sparse models (i.e. with few non-zero coefficients for each problem) that are easy to interpret. We show by simulation and application that it tends to select fewer features while achieving a similar predictive performance as compared to available methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>An implementation is available in the R package \u201csparselink\u201d (https:\/\/github.com\/rauschenberger\/sparselink, https:\/\/cran.r-project.org\/package=sparselink).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf406","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T10:03:30Z","timestamp":1752746610000},"source":"Crossref","is-referenced-by-count":1,"title":["Estimating sparse regression models in multi-task learning and transfer learning through adaptive penalisation"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6498-4801","authenticated-orcid":false,"given":"Armin","family":"Rauschenberger","sequence":"first","affiliation":[{"name":"Bioinformatics and Artificial Intelligence, Department of Medical Informatics, Luxembourg Institute of Health (LIH) , 1445 Strassen,","place":["Luxembourg"]},{"name":"Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg , 4362 Esch-sur-Alzette,","place":["Luxembourg"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3443-0298","authenticated-orcid":false,"given":"Petr V","family":"Nazarov","sequence":"additional","affiliation":[{"name":"Bioinformatics and Artificial Intelligence, Department of Medical Informatics, Luxembourg Institute of Health (LIH) , 1445 Strassen,","place":["Luxembourg"]},{"name":"Multiomics Data Science, Department of Cancer Research, Luxembourg Institute 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