{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:10:57Z","timestamp":1772165457921,"version":"3.50.1"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"crossref","award":["WI 4450\/2-1"],"award-info":[{"award-number":["WI 4450\/2-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally,\n                      <jats:italic>seagull<\/jats:italic>\n                      -the R package presented here- produces complete regularization paths.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Publicly available high-dimensional methylation data are used to compare\n                      <jats:italic>seagull<\/jats:italic>\n                      to the established R package\n                      <jats:italic>SGL<\/jats:italic>\n                      . The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results of\n                      <jats:italic>seagull<\/jats:italic>\n                      and\n                      <jats:italic>SGL<\/jats:italic>\n                      were very similar (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.99),\n                      <jats:italic>seagull<\/jats:italic>\n                      computed the solution in a fraction of the time needed by\n                      <jats:italic>SGL<\/jats:italic>\n                      . Additionally,\n                      <jats:italic>seagull<\/jats:italic>\n                      enables the incorporation of weights for each penalized feature.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The following operators for linear regression models are available in\n                      <jats:italic>seagull<\/jats:italic>\n                      : lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus,\n                      <jats:italic>seagull<\/jats:italic>\n                      is a convenient envelope of lasso variants.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-020-03725-w","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T09:03:46Z","timestamp":1600160626000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent"],"prefix":"10.1186","volume":"21","author":[{"given":"Jan","family":"Klosa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noah","family":"Simon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P\u00e5l Olof","family":"Westermark","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Volkmar","family":"Liebscher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3639-2574","authenticated-orcid":false,"given":"D\u00f6rte","family":"Wittenburg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"issue":"2","key":"3725_CR1","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. J Royal Statistical Soc B. 2005 Apr;67(2):301\u201320. https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00503.x.","journal-title":"J Royal Statistical Soc B."},{"issue":"1","key":"3725_CR2","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc B (Methodological). 1996;58(1):267\u201388.","journal-title":"J Royal Stat Soc B (Methodological)"},{"key":"3725_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13059-019-1824-y","volume":"25","author":"CG Bell","year":"2019","unstructured":"Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 2019;25:20. https:\/\/doi.org\/10.1186\/s13059-019-1824-y.","journal-title":"Genome Biol"},{"issue":"1","key":"3725_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J Royal Statistical Soc B. 2006 Feb;68(1):49\u201367. https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00532.x.","journal-title":"J Royal Statistical Soc B"},{"issue":"2","key":"3725_CR5","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1080\/10618600.2012.681250","volume":"22","author":"N Simon","year":"2013","unstructured":"Simon N, Friedman J, Hastie T, Tibshirani R. A sparse-group lasso. J Comput Graph Stat. 2013 Apr;22(2):231\u201345. https:\/\/doi.org\/10.1080\/10618600.2012.681250.","journal-title":"J Comput Graph Stat"},{"key":"3725_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/7691937","volume":"2017","author":"A-L Boulesteix","year":"2017","unstructured":"Boulesteix A-L, De Bin R, Jiang X, Fuchs M. IPF-LASSO: integrative L1 -penalized regression with penalty factors for prediction based on multi-Omics data. Comput Mathematical Methods in Med. 2017;2017:1\u201314. https:\/\/doi.org\/10.1155\/2017\/7691937.","journal-title":"Comput Mathematical Methods in Med"},{"key":"3725_CR7","unstructured":"Simon N, Friedman J, Hastie T, Tibshirani R. SGL: Fit a GLM (or Cox Model) with a Combination of Lasso and Group Lasso Regularization. 2019. https:\/\/CRAN.R-project.org\/package=SGL."},{"issue":"3","key":"3725_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh N, Boyd S. Proximal algorithms. FNT in Optimization. 2014;1(3):127\u2013239.","journal-title":"FNT in Optimization"},{"key":"3725_CR9","unstructured":"Eddelbuettel D, Francois R, Allaire JJ, Ushey K, Kou Q, Russell N, et al. Rcpp: Seamless R and C++ Integration. 2019. https:\/\/CRAN.R-project.org\/package=Rcpp."},{"issue":"4","key":"3725_CR10","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1016\/j.cmet.2017.03.016","volume":"25","author":"DA Petkovich","year":"2017","unstructured":"Petkovich DA, Podolskiy DI, Lobanov AV, Lee S-G, Miller RA, Gladyshev VN. Using DNA Methylation Profiling to Evaluate Biological Age and Longevity Interventions. 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