{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:15:24Z","timestamp":1780611324076,"version":"3.54.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one.<\/jats:p>\n               <jats:p>Results: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and\/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.<\/jats:p>\n               <jats:p>Availability: Software that implements the procedures and data source on which this paper focuses are freely available at http:\/\/www-lmc.imag.fr\/SMS\/membres\/Gersende_Fort,Sophie_Lambert.html<\/jats:p>\n               <jats:p>Contact: \u00a0sophie.lambert@imag.fr<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti114","type":"journal-article","created":{"date-parts":[[2004,11,6]],"date-time":"2004-11-06T01:14:14Z","timestamp":1099703654000},"page":"1104-1111","source":"Crossref","is-referenced-by-count":140,"title":["Classification using partial least squares with penalized logistic regression"],"prefix":"10.1093","volume":"21","author":[{"given":"Gersende","family":"Fort","sequence":"first","affiliation":[{"name":"CNRS\/LMC-IMAG BP 53, 38041 Grenoble cedex 9, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Lambert-Lacroix","sequence":"additional","affiliation":[{"name":"CNRS\/LMC-IMAG BP 53, 38041 Grenoble cedex 9, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2004,11,5]]},"reference":[{"key":"2023013107281709000_B1","doi-asserted-by":"crossref","unstructured":"Albert, A. and Anderson, J. 1984On the existence of maximum likelihood estimates in logistic regression models. Biometrika711\u201310","DOI":"10.1093\/biomet\/71.1.1"},{"key":"2023013107281709000_B2","doi-asserted-by":"crossref","unstructured":"Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A. 1999Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA966745\u20136750","DOI":"10.1073\/pnas.96.12.6745"},{"key":"2023013107281709000_B3","doi-asserted-by":"crossref","unstructured":"Antoniadis, A., Lambert-Lacroix, S., Leblanc, F. 2003Effective dimension reduction methods for tumor classification using gene expression data. Bioinformatics19563\u2013570","DOI":"10.1093\/bioinformatics\/btg062"},{"key":"2023013107281709000_B4","doi-asserted-by":"crossref","unstructured":"Devroye, L., Gyorfi, L., Lugosi, G. Theory of Pattern Recognition1996, New York  Springer-Verlag","DOI":"10.1007\/978-1-4612-0711-5"},{"key":"2023013107281709000_B5","unstructured":"Ding, B. and Gentleman, R. 2004Classification using generalized partial least squares. Technical Report 5, Bioconductor Project Working Papers.   http:\/\/www.bepress.com\/bioconductor\/paper5"},{"key":"2023013107281709000_B6","doi-asserted-by":"crossref","unstructured":"Dudoit, S., Fridlyand, J., Speed, T. 2002Comparison of discrimination methods for the classification of tumors using gene expression data. J. Amer. Stat. Assoc.97,  pp. 77\u201387","DOI":"10.1198\/016214502753479248"},{"key":"2023013107281709000_B7","doi-asserted-by":"crossref","unstructured":"Eilers, P., Boer, J., Van Ommen, G., Van Houwelingen, H. 2001Classification of microarray data with penalized logistic regression. Proceedings of SPIE. Progress in Biomedical Optics and Images  vol. 4266,  pp. 187\u2013198","DOI":"10.1117\/12.427987"},{"key":"2023013107281709000_B8","doi-asserted-by":"crossref","unstructured":"Fahrmeir, L. and Tutz, G. Multivariate Statistical Modelling Based on Generalized Linear Models2001 2nd edn , New York  Springer Series in Statistics","DOI":"10.1007\/978-1-4757-3454-6"},{"key":"2023013107281709000_B9","doi-asserted-by":"crossref","unstructured":"Frank, I. and Friedman, J. 1993A statistical view of some chemometrics regression tools, with discussion. Technometrics35,  pp. 109\u2013148","DOI":"10.1080\/00401706.1993.10485033"},{"key":"2023013107281709000_B10","doi-asserted-by":"crossref","unstructured":"Furey, T., Cristianini, N., Duffy, N., Bednarsky, D., Schummer, M., Haussler, D. 2000Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics16906\u2013914","DOI":"10.1093\/bioinformatics\/16.10.906"},{"key":"2023013107281709000_B11","unstructured":"Ghosh, D. 2002Singular value decomposition regression models for classification of tumors from microarray experiments. Pac. Symp. Biocomput.9818\u201329"},{"key":"2023013107281709000_B12","doi-asserted-by":"crossref","unstructured":"Ghosh, D. 2003Penalized discriminant methods for the classification of tumors from gene expression data. Biometrics59992\u20131000","DOI":"10.1111\/j.0006-341X.2003.00114.x"},{"key":"2023013107281709000_B13","doi-asserted-by":"crossref","unstructured":"Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E. 1999Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science286531\u2013537","DOI":"10.1126\/science.286.5439.531"},{"key":"2023013107281709000_B14","unstructured":"Green, P. 1984Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. J.R. Statist.Soc. B46149\u2013192"},{"key":"2023013107281709000_B15","unstructured":"Helland, I. 1988On the structure of partial least squares regression. Commun. Statist., Simulation Comput.17581\u2013607"},{"key":"2023013107281709000_B16","unstructured":"Huang, X. and Pan, W. 2003Linear regression and two-class classification with gene expression data. Bioinformatics192072\u20132078"},{"key":"2023013107281709000_B17","unstructured":"Kass, R. and Raftery, A. 1995Bayes factor. J. Amer. Stat. Assoc.90733\u2013795"},{"key":"2023013107281709000_B18","unstructured":"Le Cessie, S. and Van Houwelingen, J. 1992Ridge estimators in logistic regression. J. R. Statist. Soc. C41191\u2013201"},{"key":"2023013107281709000_B19","doi-asserted-by":"crossref","unstructured":"Marx, B.D. 1996Iteratively reweighted partial least squares estimation for generalized linear regression. Technometrics38374\u2013381","DOI":"10.1080\/00401706.1996.10484549"},{"key":"2023013107281709000_B20","unstructured":"Massy, W.F. 1965Principal components regression in exploratory statistical research. J. Amer. Statist. Assoc.60234\u2013246"},{"key":"2023013107281709000_B21","unstructured":"Naes, T. and Martens, H. 1985Comparison of prediction methods for multicollinear data. Commun. Statist. Simulation Comput.14545\u2013576"},{"key":"2023013107281709000_B22","doi-asserted-by":"crossref","unstructured":"Nguyen, D. and Rocke, D. 2002Tumor classification by partial least squares using microarray gene expression data. Bioinformatics1839\u201350","DOI":"10.1093\/bioinformatics\/18.1.39"},{"key":"2023013107281709000_B23","doi-asserted-by":"crossref","unstructured":"Santner, T. and Duffy, D. 1986A note on A. Albert and J.A. Anderson's conditions for the existence of maximum likelihood estimates in logistic regression models. Biometrika73755\u2013758","DOI":"10.1093\/biomet\/73.3.755"},{"key":"2023013107281709000_B24","doi-asserted-by":"crossref","unstructured":"Singh, D., Febbo, P., Ross, D., Jackson, G., Manola, J., Ladd, C., Tamayo, A., Renshaw, A., D'Amico, A.V., Richie, J., et al. 2002Gene expression correlates of clinical prostate cancer behavior. Cancer Cell1203\u2013209","DOI":"10.1016\/S1535-6108(02)00030-2"},{"key":"2023013107281709000_B25","unstructured":"Timm, N. 2002Applied Multivariate Analysis.  , New York  Springer-Verlag"},{"key":"2023013107281709000_B26","doi-asserted-by":"crossref","unstructured":"West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J., Marks, J., Nevins, J. 2001Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Natl Acad. Sci.9811462\u201311467","DOI":"10.1073\/pnas.201162998"},{"key":"2023013107281709000_B27","doi-asserted-by":"crossref","unstructured":"Wold, H. 1975Soft modelling by latent variables: the non-linear iterative partial least squares (NIPALS) approach. In Gani, J. (Ed.). Perspectives in Probability and Statistics, Papers in Honour of M. S. Bartlett , London  Academic Press,  pp. 117\u2013142","DOI":"10.1017\/S0021900200047604"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/7\/1104\/48966690\/bioinformatics_21_7_1104.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/7\/1104\/48966690\/bioinformatics_21_7_1104.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T10:34:06Z","timestamp":1675161246000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/21\/7\/1104\/268921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,11,5]]},"references-count":27,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2005,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bti114","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2005,4,1]]},"published":{"date-parts":[[2004,11,5]]}}}