{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:24:16Z","timestamp":1773303856589,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-14-CE12-0020-01"],"award-info":[{"award-number":["ANR-14-CE12-0020-01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Conseil R\u00e9gional Bourgogne, Franche-Comt\u00e9"},{"DOI":"10.13039\/501100002924","name":"FEDER","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002924","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Funding for Regional Economical Development"},{"name":"Fondation de France\/Fondation de l'\u0153il"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real datasets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Considering dimension reduction in penalized regression methods improved the prediction accuracy. The sparse group PLS reached the lowest prediction error while consistently selecting a few predictors from a single group.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>R codes for the prediction methods are freely available at https:\/\/github.com\/SoufianeAjana\/Blisar.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz135","type":"journal-article","created":{"date-parts":[[2019,2,23]],"date-time":"2019-02-23T20:14:39Z","timestamp":1550952879000},"page":"3628-3634","source":"Crossref","is-referenced-by-count":25,"title":["Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size"],"prefix":"10.1093","volume":"35","author":[{"given":"Soufiane","family":"Ajana","sequence":"first","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Niyazi","family":"Acar","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Lionel","family":"Bretillon","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Boris P","family":"Hejblum","sequence":"additional","affiliation":[{"name":"ISPED, Inserm, Bordeaux Population Health Research Center 1219, Inria SISTM, University of Bordeaux , F-33000 Bordeaux, France"},{"name":"Vaccine Research Institute (VRI), H\u00f4pital Henri Mondor , Cr\u00e9teil, France"}]},{"given":"H\u00e9l\u00e8ne","family":"Jacqmin-Gadda","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team Biostatistics, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"C\u00e9cile","family":"Delcourt","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"name":"for the BLISAR Study Group","sequence":"additional","affiliation":[]},{"given":"Niyazi","family":"Acar","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Soufiane","family":"Ajana","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Olivier","family":"Berdeaux","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Sylvain","family":"Bouton","sequence":"additional","affiliation":[{"name":"Laboratoires Th\u00e9a, Clermont-Ferrand , France"}]},{"given":"Lionel","family":"Bretillon","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Alain","family":"Bron","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"},{"name":"Department of Ophthalmology, University Hospital , Dijon, France"}]},{"given":"Benjamin","family":"Buaud","sequence":"additional","affiliation":[{"name":"ITERG\u2014Equipe Nutrition M\u00e9tabolisme & Sant\u00e9 , Bordeaux, France"}]},{"given":"St\u00e9phanie","family":"Cabaret","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Audrey","family":"Cougnard-Gr\u00e9goire","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Catherine","family":"Creuzot-Garcher","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"},{"name":"Department of Ophthalmology, University Hospital , Dijon, France"}]},{"given":"C\u00e9cile","family":"Delcourt","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Marie-Noelle","family":"Delyfer","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"},{"name":"Service d\u2019Ophtalmologie, CHU de Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Catherine","family":"F\u00e9art-Couret","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Val\u00e9rie","family":"Febvret","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"St\u00e9phane","family":"Gr\u00e9goire","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"Zhiguo","family":"He","sequence":"additional","affiliation":[{"name":"Laboratory for Biology, Imaging, and Engineering of Corneal Grafts, EA2521, Faculty of Medicine, University Jean Monnet , Saint-Etienne, France"}]},{"given":"Jean-Fran\u00e7ois","family":"Korobelnik","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"},{"name":"Service d\u2019Ophtalmologie, CHU de Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Lucy","family":"Martine","sequence":"additional","affiliation":[{"name":"Centre des Sciences du Go\u00fbt et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Universit\u00e9 Bourgogne Franche-Comt\u00e9 , Dijon, France"}]},{"given":"B\u00e9n\u00e9dicte","family":"Merle","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux , F-33000 Bordeaux, France"}]},{"given":"Carole","family":"Vaysse","sequence":"additional","affiliation":[{"name":"ITERG\u2014Equipe Nutrition M\u00e9tabolisme & Sant\u00e9 , Bordeaux, France"}]}],"member":"286","published-online":{"date-parts":[[2019,4,1]]},"reference":[{"key":"2023013108123299700_btz135-B1","doi-asserted-by":"crossref","first-page":"e35102","DOI":"10.1371\/journal.pone.0035102","article-title":"Lipid composition of the human eye: are red blood cells a good mirror of retinal and optic nerve fatty acids?","volume":"7","author":"Acar","year":"2012","journal-title":"PLoS One"},{"key":"2023013108123299700_btz135-B2","first-page":"126","article-title":"Comparison of regularized regression methods for \u223comics data","volume":"3","author":"Acharjee","year":"2013","journal-title":"Metabol."},{"key":"2023013108123299700_btz135-B3","doi-asserted-by":"crossref","first-page":"6562","DOI":"10.1073\/pnas.102102699","article-title":"Selection bias in gene extraction on the basis of microarray gene-expression data","volume":"99","author":"Ambroise","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023013108123299700_btz135-B4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/09-SS054","article-title":"A survey of cross-validation procedures for model selection","volume":"4","author":"Arlot","year":"2010","journal-title":"Stat. Surv"},{"key":"2023013108123299700_btz135-B5","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1093\/bioinformatics\/btu660","article-title":"Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data","volume":"31","author":"Bastien","year":"2015","journal-title":"Bioinformatics"},{"key":"2023013108123299700_btz135-B6","doi-asserted-by":"crossref","first-page":"47.","DOI":"10.1186\/s13321-014-0047-1","article-title":"Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation","volume":"6","author":"Baumann","year":"2014","journal-title":"J. Cheminf"},{"key":"2023013108123299700_btz135-B7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1002\/bimj.200900064","article-title":"High-dimensional cox models: the choice of penalty as part of the model building process","volume":"52","author":"Benner","year":"2010","journal-title":"Biom. J"},{"key":"2023013108123299700_btz135-B8","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1016\/j.chroma.2010.10.039","article-title":"Identification and quantification of phosphatidylcholines containing very-long-chain polyunsaturated fatty acid in bovine and human retina using liquid chromatography\/tandem mass spectrometry","volume":"1217","author":"Berdeaux","year":"2010","journal-title":"J. Chromatogr. A"},{"key":"2023013108123299700_btz135-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sab.2015.02.003","article-title":"A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy","volume":"107","author":"Boucher","year":"2015","journal-title":"Spectrochim. Acta B Atomic Spectr"},{"key":"2023013108123299700_btz135-B10","doi-asserted-by":"crossref","first-page":"Article33.","DOI":"10.2202\/1544-6115.1075","article-title":"PLS dimension reduction for classification with microarray data","volume":"3","author":"Boulesteix","year":"2004","journal-title":"Stat. Appl. Genet. Mol. Biol"},{"key":"2023013108123299700_btz135-B11","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.exer.2008.08.010","article-title":"Lipid and fatty acid profile of the retina, retinal pigment epithelium\/choroid, and the lacrimal gland, and associations with adipose tissue fatty acids in human subjects","volume":"87","author":"Bretillon","year":"2008","journal-title":"Exp. Eye Res"},{"key":"2023013108123299700_btz135-B12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/j.1467-9868.2009.00723.x","article-title":"Sparse partial least squares regression for simultaneous dimension reduction and variable selection","volume":"72","author":"Chun","year":"2010","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol"},{"key":"2023013108123299700_btz135-B13","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/nrc2294","article-title":"The properties of high-dimensional data spaces: implications for exploring gene and protein expression data","volume":"8","author":"Clarke","year":"2008","journal-title":"Nat. Rev. Cancer"},{"key":"2023013108123299700_btz135-B14","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1080\/00949655.2014.938241","article-title":"Bi-level variable selection via adaptive sparse group Lasso","volume":"85","author":"Fang","year":"2015","journal-title":"J. Stat. Comput. Simul"},{"key":"2023013108123299700_btz135-B15","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/TCBB.2011.139","article-title":"The LASSO and sparse least square regression methods for SNP selection in predicting quantitative traits","volume":"9","author":"Feng","year":"2012","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform"},{"key":"2023013108123299700_btz135-B16","doi-asserted-by":"crossref","first-page":"130.","DOI":"10.1007\/s11306-017-1275-y","article-title":"Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics","volume":"13","author":"F\u00e9raud","year":"2017","journal-title":"Metabolomics"},{"key":"2023013108123299700_btz135-B17","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1002\/cem.1418","article-title":"Review of sparse methods in regression and classification with application to chemometrics","volume":"26","author":"Filzmoser","year":"2012","journal-title":"J. Chemometr"},{"key":"2023013108123299700_btz135-B18","author":"Friedman","year":"2010"},{"key":"2023013108123299700_btz135-B19","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1093\/bioinformatics\/btt608","article-title":"Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data","volume":"30","author":"Garcia","year":"2014","journal-title":"Bioinformatics"},{"key":"2023013108123299700_btz135-B20","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recogn. Lett"},{"key":"2023013108123299700_btz135-B21","author":"G\u00e9ron","year":"2017"},{"key":"2023013108123299700_btz135-B22","doi-asserted-by":"crossref","DOI":"10.1201\/b18401","volume-title":"Statistical Learning with Sparsity: The Lasso and Generalizations 1 Edition","author":"Hastie","year":"2015"},{"key":"2023013108123299700_btz135-B23","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning \u2013 Data Mining","author":"Hastie","year":"2001"},{"key":"2023013108123299700_btz135-B24","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009","edition":"5"},{"key":"2023013108123299700_btz135-B25","author":"Huang","year":"2009"},{"key":"2023013108123299700_btz135-B26","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1038\/ijo.2015.214","article-title":"The importance of prediction model validation and assessment in obesity and nutrition research","volume":"40","author":"Ivanescu","year":"2016","journal-title":"Int J Obes (Lond)"},{"key":"2023013108123299700_btz135-B27","volume-title":"An Introduction to Statistical Learning: With Applications in R 1st ed. 2013, Corr","author":"James","year":"2017","edition":"7"},{"key":"2023013108123299700_btz135-B28","doi-asserted-by":"crossref","first-page":"Article 35.","DOI":"10.2202\/1544-6115.1390","article-title":"A sparse PLS for variable selection when integrating omics data","volume":"7","author":"L\u00ea Cao","year":"2008","journal-title":"Stat. Appl. Genet. Mol. Biol"},{"key":"2023013108123299700_btz135-B29","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1093\/bioinformatics\/btp515","article-title":"integrOmics: an R package to unravel relationships between two omics datasets","volume":"25","author":"L\u00ea Cao","year":"2009","journal-title":"Bioinformatics"},{"key":"2023013108123299700_btz135-B30","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1002\/eji.201344433","article-title":"Dendritic cell-based therapeutic vaccine elicits polyfunctional HIV-specific T-cell immunity associated with control of viral load: clinical immunology","volume":"44","author":"L\u00e9vy","year":"2014","journal-title":"Eur. J. Immunol"},{"key":"2023013108123299700_btz135-B31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/bioinformatics\/btv535","article-title":"Group and sparse group partial least square approaches applied in genomics context","volume":"32","author":"Liquet","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013108123299700_btz135-B32","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1198\/tas.2011.11052","article-title":"Empirical performance of cross-validation with oracle methods in a genomics context","volume":"65","author":"Martinez","year":"2011","journal-title":"Am. Stat"},{"key":"2023013108123299700_btz135-B33","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1002\/cem.887","article-title":"Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)","volume":"18","author":"Mevik","year":"2004","journal-title":"J. Chemometr"},{"key":"2023013108123299700_btz135-B34","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.1093\/bioinformatics\/bti499","article-title":"Prediction error estimation: a comparison of resampling methods","volume":"21","author":"Molinaro","year":"2005","journal-title":"Bioinformatics"},{"key":"2023013108123299700_btz135-B35","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1002\/cem.676","article-title":"Understanding the collinearity problem in regression and discriminant analysis","volume":"15","author":"Naes","year":"2001","journal-title":"J. Chemometr"},{"key":"2023013108123299700_btz135-B36","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.talanta.2016.10.062","article-title":"Advanced predictive methods for wine age prediction: part I \u2013 a comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods","volume":"171","author":"Rendall","year":"2017","journal-title":"Talanta"},{"key":"2023013108123299700_btz135-B37","author":"Sill","year":"2009"},{"key":"2023013108123299700_btz135-B38","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/10618600.2012.681250","article-title":"A Sparse-Group Lasso","volume":"22","author":"Simon","year":"2013","journal-title":"J. Comput. Graph. Stat"},{"key":"2023013108123299700_btz135-B39","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.aca.2007.04.043","article-title":"Assessing the statistical validity of proteomics based biomarkers","volume":"592","author":"Smit","year":"2007","journal-title":"Anal. Chim. Acta"},{"key":"2023013108123299700_btz135-B40","volume-title":"Introduction to Linear Algebra","author":"Strang","year":"2016","edition":"5th edn."},{"key":"2023013108123299700_btz135-B41","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the Lasso","volume":"58","author":"Tibshirani","year":"1994","journal-title":"J. R. Stat. Soc, Ser. B"},{"key":"2023013108123299700_btz135-B42","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/JPROC.2010.2044010","article-title":"Computational methods for sparse solution of linear inverse problems","volume":"98","author":"Tropp","year":"2010","journal-title":"Proc. IEEE"},{"key":"2023013108123299700_btz135-B43","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1002\/cem.695","article-title":"Orthogonal projections to latent structures (O-PLS)","volume":"16","author":"Trygg","year":"2002","journal-title":"J. Chemometr"},{"key":"2023013108123299700_btz135-B44","first-page":"1369","article-title":"Consistent group selection in high-dimensional linear regression","volume":"16","author":"Wei","year":"2010","journal-title":"Bernoulli (Andover)"},{"key":"2023013108123299700_btz135-B45","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw"},{"key":"2023013108123299700_btz135-B46","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1145\/2623330.2623635","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Xu","year":"2014"},{"key":"2023013108123299700_btz135-B47","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","article-title":"Model selection and estimation in regression with grouped variables","volume":"68","author":"Yuan","year":"2006","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"2023013108123299700_btz135-B48","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1080\/02664763.2016.1254731","article-title":"A link-free sparse group variable selection method for single-index model","volume":"44","author":"Zeng","year":"2017","journal-title":"J. Appl. Stat"},{"key":"2023013108123299700_btz135-B49","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1111\/rssb.12100","article-title":"Variable selection for support vector machines in moderately high dimensions","volume":"78","author":"Zhang","year":"2016","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol"},{"key":"2023013108123299700_btz135-B50","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1198\/016214506000000735","article-title":"The adaptive Lasso and its Oracle properties","volume":"101","author":"Zou","year":"2006","journal-title":"J. Am. Stat. Assoc"},{"key":"2023013108123299700_btz135-B51","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/19\/3628\/48975757\/bioinformatics_35_19_3628.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/19\/3628\/48975757\/bioinformatics_35_19_3628.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T02:26:45Z","timestamp":1721010405000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/19\/3628\/5372340"}},"subtitle":[],"editor":[{"given":"Janet","family":"Kelso","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,4,1]]},"references-count":51,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2019,10,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz135","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,10,1]]},"published":{"date-parts":[[2019,4,1]]}}}