{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T05:07:24Z","timestamp":1768712844076,"version":"3.49.0"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation : A vast literature from the past decade is devoted to relating gene profiles and subject survival or time to cancer recurrence. Biomarker discovery from high-dimensional data, such as transcriptomic or single nucleotide polymorphism profiles, is a major challenge in the search for more precise diagnoses. The proportional hazard regression model suggested by Cox (1972), to study the relationship between the time to event and a set of covariates in the presence of censoring is the most commonly used model for the analysis of survival data. However, like multivariate regression, it supposes that more observations than variables, complete data, and not strongly correlated variables are available. In practice, when dealing with high-dimensional data, these constraints are crippling. Collinearity gives rise to issues of over-fitting and model misidentification. Variable selection can improve the estimation accuracy by effectively identifying the subset of relevant predictors and enhance the model interpretability with parsimonious representation. To deal with both collinearity and variable selection issues, many methods based on least absolute shrinkage and selection operator penalized Cox proportional hazards have been proposed since the reference paper of Tibshirani. Regularization could also be performed using dimension reduction as is the case with partial least squares (PLS) regression. We propose two original algorithms named sPLSDR and its non-linear kernel counterpart DKsPLSDR, by using sparse PLS regression (sPLS) based on deviance residuals. We compared their predicting performance with state-of-the-art algorithms on both simulated and real reference benchmark datasets.<\/jats:p><jats:p>Results : sPLSDR and DKsPLSDR compare favorably with other methods in their computational time, prediction and selectivity, as indicated by results based on benchmark datasets. Moreover, in the framework of PLS regression, they feature other useful tools, including biplots representation, or the ability to deal with missing data. Therefore, we view them as a useful addition to the toolbox of estimation and prediction methods for the widely used Cox\u2019s model in the high-dimensional and low-sample size settings.<\/jats:p><jats:p>Availability and implementation : The R-package plsRcox is available on the CRAN and is maintained by Fr\u00e9d\u00e9ric Bertrand. http:\/\/cran.r-project.org\/web\/packages\/plsRcox\/index.html .<\/jats:p><jats:p>Contact : pbastien@rd.loreal.com or fbertran@math.unistra.fr .<\/jats:p><jats:p>Supplementary information : Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu660","type":"journal-article","created":{"date-parts":[[2014,10,7]],"date-time":"2014-10-07T04:41:50Z","timestamp":1412656910000},"page":"397-404","source":"Crossref","is-referenced-by-count":55,"title":["Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data"],"prefix":"10.1093","volume":"31","author":[{"given":"Philippe","family":"Bastien","sequence":"first","affiliation":[{"name":"1 L\u2019Or\u00e9al Recherche & Innovation, 93601 Aulnay-sous-Bois, 2 IRMA, CNRS UMR 7501, Labex IRMIA, Universit\u00e9 de Strasbourg, 67084 Strasbourg Cedex, 3 INSERM EA3430, Laboratoire de Biostatistique, Facult\u00e9 de M\u00e9decine de Strasbourg, Labex IRMIA, Universit\u00e9 de Strasbourg, 67085 Strasbourg Cedex, France"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Bertrand","sequence":"additional","affiliation":[{"name":"1 L\u2019Or\u00e9al Recherche & Innovation, 93601 Aulnay-sous-Bois, 2 IRMA, CNRS UMR 7501, Labex IRMIA, Universit\u00e9 de Strasbourg, 67084 Strasbourg Cedex, 3 INSERM EA3430, Laboratoire de Biostatistique, Facult\u00e9 de M\u00e9decine de Strasbourg, Labex IRMIA, Universit\u00e9 de Strasbourg, 67085 Strasbourg Cedex, France"}]},{"given":"Nicolas","family":"Meyer","sequence":"additional","affiliation":[{"name":"1 L\u2019Or\u00e9al Recherche & Innovation, 93601 Aulnay-sous-Bois, 2 IRMA, CNRS UMR 7501, Labex IRMIA, Universit\u00e9 de Strasbourg, 67084 Strasbourg Cedex, 3 INSERM EA3430, Laboratoire de Biostatistique, Facult\u00e9 de M\u00e9decine de Strasbourg, Labex IRMIA, Universit\u00e9 de Strasbourg, 67085 Strasbourg Cedex, France"}]},{"given":"Myriam","family":"Maumy-Bertrand","sequence":"additional","affiliation":[{"name":"1 L\u2019Or\u00e9al Recherche & Innovation, 93601 Aulnay-sous-Bois, 2 IRMA, CNRS UMR 7501, Labex IRMIA, Universit\u00e9 de Strasbourg, 67084 Strasbourg Cedex, 3 INSERM EA3430, Laboratoire de Biostatistique, Facult\u00e9 de M\u00e9decine de Strasbourg, Labex IRMIA, Universit\u00e9 de Strasbourg, 67085 Strasbourg Cedex, France"}]}],"member":"286","published-online":{"date-parts":[[2014,10,6]]},"reference":[{"key":"2023020116163339000_btu660-B1","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1214\/aos\/1176325630","article-title":"Nearest neighbor estimation of a bivariate distribution under random censoring","volume":"22","author":"Akritas","year":"1994","journal-title":"Ann. 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Stat."},{"key":"2023020116163339000_btu660-B35","article-title":"Partial least squares and cox model with application to gene expression","author":"Lambert-Lacroix","year":"2011","journal-title":"Technical report"},{"key":"2023020116163339000_btu660-B36","doi-asserted-by":"crossref","first-page":"e61505","DOI":"10.1371\/journal.pone.0061505","article-title":"When is hub gene selection better than standard meta-analysis?","volume":"8","author":"Langfelder","year":"2013","journal-title":"PloS One"},{"key":"2023020116163339000_btu660-B37","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1093\/bioinformatics\/bth900","article-title":"Partial cox regression analysis for high-dimensional microarray gene expression data","volume":"20","author":"Li","year":"2004","journal-title":"Bioinformatics"},{"key":"2023020116163339000_btu660-B38","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1093\/bioinformatics\/bti824","article-title":"Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2023020116163339000_btu660-B39","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1002\/cem.1180070104","article-title":"The kernel algorithm for PLS","volume":"7","author":"Lindgren","year":"1993","journal-title":"J. Chemom."},{"key":"2023020116163339000_btu660-B40","first-page":"2739","article-title":"The role of proxy genes in predictive models: an application to early detection of prostate cancer","volume-title":"Joint Statistical Meetings Proceedings","author":"Magidson","year":"2010"},{"key":"2023020116163339000_btu660-B41","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1080\/00401706.1996.10484549","article-title":"Iteratively reweighted partial least squares estimation for generalized linear regression","volume":"38","author":"Marx","year":"1996","journal-title":"Technometrics"},{"key":"2023020116163339000_btu660-B42","doi-asserted-by":"crossref","first-page":"4193","DOI":"10.1182\/blood-2008-02-134411","article-title":"An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia","volume":"112","author":"Metzeler","year":"2008","journal-title":"Blood"},{"key":"2023020116163339000_btu660-B43","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1093\/bioinformatics\/18.12.1625","article-title":"Partial least squares proportional hazard regression for application to DNA microarray survival data","volume":"18","author":"Nguyen","year":"2002","journal-title":"Bioinformatics"},{"key":"2023020116163339000_btu660-B44","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1111\/j.1467-9868.2007.00607.x","article-title":"L \u00a01 regularization path algorithm for generalized linear models","volume":"69","author":"Park","year":"2007","journal-title":"J. R. Stat. Soc. B"},{"key":"2023020116163339000_btu660-B45","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/cem.1180080204","article-title":"A PLS kernel algorithm for data sets with many variables and fewer objects. Part I: theory and algorithm","volume":"8","author":"R\u00e4nnar","year":"1994","journal-title":"J. Chemom."},{"key":"2023020116163339000_btu660-B46","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1186\/1471-2407-10-561","article-title":"Allelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatment","volume":"10","author":"Romain","year":"2010","journal-title":"BMC Cancer"},{"key":"2023020116163339000_btu660-B47","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1056\/NEJMoa012914","article-title":"The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma","volume":"346","author":"Rosenwald","year":"2002","journal-title":"N. Engl. J. Med."},{"key":"2023020116163339000_btu660-B48","first-page":"97","article-title":"Kernel partial least squares regression in reproducing kernel hilbert space","volume":"2","author":"Rosipal","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"2023020116163339000_btu660-B49","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1111\/j.1541-0420.2010.01459.x","article-title":"A robust alternative to the Schemper-Henderson estimator of prediction error","volume":"67","author":"Schmid","year":"2011","journal-title":"Biometrics"},{"key":"2023020116163339000_btu660-B50","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1093\/biostatistics\/kxj006","article-title":"Microarray gene expression data with linked survival phenotypes: diffuse large-Bcell lymphoma revisited","volume":"7","author":"Segal","year":"2006","journal-title":"Biostatistics"},{"key":"2023020116163339000_btu660-B51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v039.i05","article-title":"Regularization paths for cox\u2019s proportional hazards model via coordinate descent","volume":"39","author":"Simon","year":"2011","journal-title":"J. Stat. Softw."},{"key":"2023020116163339000_btu660-B52","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":"2023020116163339000_btu660-B53","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1093\/bioinformatics\/btp322","article-title":"Gradient lasso for cox proportional hazards model","volume":"25","author":"Sohn","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020116163339000_btu660-B54","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.1016\/j.csda.2007.01.004","article-title":"Kernel logistic PLS: a tool for supervised nonlinear dimensionality reduction and binary classification","volume":"51","author":"Tenenhaus","year":"2007","journal-title":"Comput. Stat. Data Anal."},{"key":"2023020116163339000_btu660-B55","volume-title":"La r\u00e9gression PLS","author":"Tenenhaus","year":"1998"},{"key":"2023020116163339000_btu660-B56","first-page":"721","article-title":"La regression logistique PLS","volume-title":"Proceedings of the 32\u00e8mes journ\u00e9es de Statistique de la Soci\u00e9t\u00e9 fran\u00e7aise de Statistique","author":"Tenenhaus","year":"1999"},{"key":"2023020116163339000_btu660-B57","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":"1996","journal-title":"J. R. Stat. Soc. B"},{"key":"2023020116163339000_btu660-B58","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3","article-title":"The lasso method for variable selection in the Cox model","volume":"16","author":"Tibshirani","year":"1997","journal-title":"Stat. Med."},{"key":"2023020116163339000_btu660-B59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2202\/1544-6115.1438","article-title":"Univariate shrinkage in the cox model for high dimensional data","volume":"8","author":"Tibshirani","year":"2009","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"2023020116163339000_btu660-B60","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1016\/j.csda.2008.05.021","article-title":"Survival prediction using gene expression data: a review and comparison","volume":"53","author":"van Wieringen","year":"2009","journal-title":"Comput. Stat. 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