{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T21:48:01Z","timestamp":1784843281895,"version":"3.55.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2008,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.sourceforge.net\/projects\/kopls\/\" ext-link-type=\"uri\">http:\/\/www.sourceforge.net\/projects\/kopls\/<\/jats:ext-link>. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-9-106","type":"journal-article","created":{"date-parts":[[2008,4,15]],"date-time":"2008-04-15T06:13:44Z","timestamp":1208240024000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space"],"prefix":"10.1186","volume":"9","author":[{"given":"Max","family":"Bylesj\u00f6","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattias","family":"Rantalainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy K","family":"Nicholson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elaine","family":"Holmes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Trygg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2008,2,19]]},"reference":[{"key":"2091_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1002\/cem.695","volume":"16","author":"J Trygg","year":"2002","unstructured":"Trygg J, Wold S: Orthogonal projections to latent structures (O-PLS). J Chemometrics 2002, 16: 119\u2013128. 10.1002\/cem.695","journal-title":"J Chemometrics"},{"key":"2091_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1002\/cem.775","volume":"17","author":"J Trygg","year":"2003","unstructured":"Trygg J, Wold S: O2-PLS, a two-block (X-Y) latent variable regression (LVR) method with an integral OSC filter. J Chemometrics 2003, 17: 53\u201364. 10.1002\/cem.775","journal-title":"J Chemometrics"},{"key":"2091_CR3","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/1471-2105-8-207","volume":"8","author":"M Bylesj\u00f6","year":"2007","unstructured":"Bylesj\u00f6 M, Eriksson D, Sj\u00f6din A, Jansson S, Moritz T, Trygg J: Orthogonal Projections to Latent Structures as a Strategy for Microarray Data Normalization. BMC Bioinformatics 2007, 8: 207. 10.1186\/1471-2105-8-207","journal-title":"BMC Bioinformatics"},{"key":"2091_CR4","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1002\/cem.1006","volume":"20","author":"M Bylesj\u00f6","year":"2006","unstructured":"Bylesj\u00f6 M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J: OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemometrics 2006, 20: 341\u2013351. 10.1002\/cem.1006","journal-title":"J Chemometrics"},{"issue":"2","key":"2091_CR5","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1021\/ac048803i","volume":"77","author":"O Cloarec","year":"2005","unstructured":"Cloarec O, Dumas ME, Trygg J, Craig A, Barton RH, Lindon JC, Nicholson JK, Holmes E: Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal Chem 2005, 77(2):517\u2013526. 10.1021\/ac048803i","journal-title":"Anal Chem"},{"issue":"5","key":"2091_CR6","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1021\/ac048630x","volume":"77","author":"O Cloarec","year":"2005","unstructured":"Cloarec O, Dumas ME, Craig A, Barton RH, Trygg J, Hudson J, Blancher C, Gauguier D, Lindon JC, Holmes E, Nicholson J: Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal Chem 2005, 77(5):1282\u20131289. 10.1021\/ac048630x","journal-title":"Anal Chem"},{"key":"2091_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0169-7439(92)80088-L","volume":"14","author":"OM Kvalheim","year":"1992","unstructured":"Kvalheim OM: The latent variable. Chemometrics Intell Lab Syst 1992, 14: 1\u20133. 10.1016\/0169-7439(92)80088-L","journal-title":"Chemometrics Intell Lab Syst"},{"key":"2091_CR8","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel methods for pattern analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor J, Cristianini N: Kernel methods for pattern analysis. Cambridge , Cambridge University Press; 2004:462."},{"key":"2091_CR9","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf B, Smola A: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge , MIT Press; 2001."},{"issue":"3","key":"2091_CR10","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s521-001-8051-z","volume":"10","author":"R Rosipal","year":"2001","unstructured":"Rosipal R, Girolami M, Trejo LJ, Cichocki A: Kernel PCA for feature extraction and de-noising in nonlinear regression. Neural Comput Appl 2001, 10(3):231\u2013243. 10.1007\/s521-001-8051-z","journal-title":"Neural Comput Appl"},{"issue":"5","key":"2091_CR11","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1162\/089976698300017467","volume":"10","author":"B Sch\u00f6lkopf","year":"1998","unstructured":"Sch\u00f6lkopf B, Smola A, M\u00fcller KR: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 1998, 10(5):1299\u20131319. 10.1162\/089976698300017467","journal-title":"Neural Comput"},{"issue":"1","key":"2091_CR12","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1002\/cem.1180070104","volume":"7","author":"F Lindgren","year":"1993","unstructured":"Lindgren F, Geladi P, Wold S: The kernel algorithm for PLS. J Chemometrics 1993, 7(1):45\u201359. 10.1002\/cem.1180070104","journal-title":"J Chemometrics"},{"issue":"2","key":"2091_CR13","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1162\/15324430260185556","volume":"2","author":"R Rosipal","year":"2002","unstructured":"Rosipal R, Trejo LJ: Kernel partial least squares regression in Reproducing Kernel Hilbert Space. J Mach Learn Res 2002, 2(2):97\u2013123. 10.1162\/15324430260185556","journal-title":"J Mach Learn Res"},{"issue":"2","key":"2091_CR14","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1021\/pr0255654","volume":"2","author":"DC Anderson","year":"2003","unstructured":"Anderson DC, Li W, Payan DG, Noble WS: A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS\/MS spectra and SEQUEST scores. J Proteome Res 2003, 2(2):137\u2013146. 10.1021\/pr0255654","journal-title":"J Proteome Res"},{"issue":"1","key":"2091_CR15","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1073\/pnas.97.1.262","volume":"97","author":"MP Brown","year":"2000","unstructured":"Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M Jr., Haussler D: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 2000, 97(1):262\u2013267. 10.1073\/pnas.97.1.262","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"10","key":"2091_CR16","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1093\/bioinformatics\/16.10.906","volume":"16","author":"TS Furey","year":"2000","unstructured":"Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906\u2013914. 10.1093\/bioinformatics\/16.10.906","journal-title":"Bioinformatics"},{"issue":"17","key":"2091_CR17","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1093\/bioinformatics\/bth383","volume":"20","author":"N Pochet","year":"2004","unstructured":"Pochet N, De Smet F, Suykens JA, De Moor BL: Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics 2004, 20(17):3185\u20133195. 10.1093\/bioinformatics\/bth383","journal-title":"Bioinformatics"},{"key":"2091_CR18","first-page":"821","volume":"25","author":"M Aizerman","year":"1964","unstructured":"Aizerman M, Braverman E, Rozonoer L: Theoretical foundations of the potential function method in pattern recognition learning. Automat Rem Contr 1964, 25: 821\u2013837.","journal-title":"Automat Rem Contr"},{"key":"2091_CR19","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1080\/00401706.1978.10489693","volume":"20","author":"S Wold","year":"1978","unstructured":"Wold S: Cross Validatory Estimation of the Number of Components in Factor and Principal Components Models. Technometrics 1978, 20: 397\u2013406. 10.2307\/1267639","journal-title":"Technometrics"},{"issue":"4598","key":"2091_CR20","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick S, Gelatt CD Jr., Vecchi MP: Optimization by Simulated Annealing. Science 1983, 220(4598):671\u2013680. 10.1126\/science.220.4598.671","journal-title":"Science"},{"key":"2091_CR21","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1002\/cem.1071","volume":"21","author":"M Rantalainen","year":"2007","unstructured":"Rantalainen M, Bylesj\u00f6 M, Cloarec O, Nicholson JK, Holmes E, Trygg J: Kernel-based orthogonal projections to latent structures (K-OPLS). J Chemometrics 2007, 21: 376\u2013385. 10.1002\/cem.1071","journal-title":"J Chemometrics"},{"issue":"5-7","key":"2091_CR22","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1002\/cem.937","volume":"19","author":"T Czekaj","year":"2005","unstructured":"Czekaj T, Wu W, Walczak B: About kernel latent variable approaches and SVM. J Chemometrics 2005, 19(5\u20137):341\u2013354. 10.1002\/cem.937","journal-title":"J Chemometrics"},{"key":"2091_CR23","unstructured":"The Comprehensive R Archive Network (CRAN)[http:\/\/cran.r-project.org\/]"},{"key":"2091_CR24","unstructured":"SVM and Kernel Methods Matlab Toolbox[http:\/\/asi.insa-rouen.fr\/enseignants\/~arakotom\/toolbox\/index.html]"},{"key":"2091_CR25","unstructured":"Least Squares - Support Vector Machines MATLAB\/C toolbox[http:\/\/www.esat.kuleuven.ac.be\/sista\/lssvmlab\/home.html]"},{"key":"2091_CR26","unstructured":"libsvm[http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm\/]"},{"key":"2091_CR27","unstructured":"kernel-machines.org[http:\/\/www.kernel-machines.org\/software]"},{"key":"2091_CR28","unstructured":"The R project for Statistical Computing[http:\/\/www.r-project.org\/]"},{"issue":"422","key":"2091_CR29","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","volume":"88","author":"J Shao","year":"1993","unstructured":"Shao J: Linear-Model Selection by Cross-Validation. J Am Stat Assoc 1993, 88(422):486\u2013494. 10.2307\/2290328","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"2091_CR30","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1111\/j.1467-7652.2005.00129.x","volume":"3","author":"S Wiklund","year":"2005","unstructured":"Wiklund S, Karlsson M, Antti H, Johnels D, Sj\u00f6str\u00f6m M, Wingsle G, Edlund U: A new metabonomic strategy for analysing the growth process of the poplar tree. Plant Biotechnol J 2005, 3(3):353\u2013362. 10.1111\/j.1467-7652.2005.00129.x","journal-title":"Plant Biotechnol J"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-9-106.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T18:28:38Z","timestamp":1738175318000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-9-106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,2,19]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2008,12]]}},"alternative-id":["2091"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-9-106","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,2,19]]},"assertion":[{"value":"28 August 2007","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2008","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2008","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"106"}}