{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T03:42:50Z","timestamp":1777693370604,"version":"3.51.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2012,2,19]],"date-time":"2012-02-19T00:00:00Z","timestamp":1329609600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2013,5]]},"DOI":"10.1007\/s00521-012-0885-6","type":"journal-article","created":{"date-parts":[[2012,2,18]],"date-time":"2012-02-18T01:59:56Z","timestamp":1329530396000},"page":"1193-1200","source":"Crossref","is-referenced-by-count":24,"title":["Partly adaptive elastic net and its application to microarray classification"],"prefix":"10.1007","volume":"22","author":[{"given":"Juntao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingmin","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,2,19]]},"reference":[{"issue":"457","key":"885_CR1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","volume":"97","author":"S Dudoit","year":"2002","unstructured":"Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for classification of tumors using gene expression data. J Am Stat Assoc 97(457):77\u201387","journal-title":"J Am Stat Assoc"},{"issue":"5439","key":"885_CR2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"T Golub","year":"1999","unstructured":"Golub T et\u00a0al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013536","journal-title":"Science"},{"issue":"1","key":"885_CR3","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389\u2013422","journal-title":"Machine Learning"},{"issue":"1","key":"885_CR4","first-page":"49","volume":"16","author":"J Zhu","year":"2004","unstructured":"Zhu J, Rosset S, Hastie T, Tibshirani R (2004) 1-norm support vector machines. Adv Neural Inform Process Syst 16(1):49\u201356","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"19","key":"885_CR5","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1093\/bioinformatics\/btl386","volume":"22","author":"GC Cawley","year":"2006","unstructured":"Cawley GC, Talbot NLC (2006) Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics 22(19):2438\u20132355","journal-title":"Bioinformatics"},{"key":"885_CR6","first-page":"841","volume":"8","author":"GC Cawley","year":"2007","unstructured":"Cawley GC, Talbot NLC (2007) Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters. J Mach Learn Res 8:841\u2013861","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"885_CR7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B 68(1):49\u201367","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"885_CR8","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1093\/biostatistics\/kxm010","volume":"9","author":"M Park","year":"2008","unstructured":"Park M, Hastie T (2008) Penalized logistic regression for detecting gene interactions. Biostatistics 9:30\u201350","journal-title":"Biostatistics"},{"issue":"2","key":"885_CR9","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67(2):301\u2013320","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"885_CR10","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/btm579","volume":"24","author":"L Wang","year":"2008","unstructured":"Wang L, Zhu J, Zou H (2008) Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 24:412\u2013419","journal-title":"Bioinformatics"},{"key":"885_CR11","doi-asserted-by":"crossref","first-page":"976","DOI":"10.3724\/SP.J.1004.2010.00976","volume":"36","author":"J Li","year":"2010","unstructured":"Li J, Jia Y (2010) An improved elastic net for cancer classification and gene selection. Acta Automatica Sinica 36:976\u2013981","journal-title":"Acta Automatica Sinica"},{"key":"885_CR12","unstructured":"Li J, Jia Y, Du J, Yu F (2009) Adaptive multi-class support vector machine for microarray classification and gene selection. ICROS-SICE international joint conference 2009. Fukuoka International Congress Center, Japan, pp 2658\u20132663"},{"issue":"4","key":"885_CR13","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/03081079.2010.530027","volume":"40","author":"M You","year":"2011","unstructured":"You M, Li GZ (2011) Feature selection for multi-class problems by using pairwise-class and all-class techniques. Int J General Syst 40(4):381\u2013394","journal-title":"International Journal of General System"},{"key":"885_CR14","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s00521-008-0202-6","volume":"18","author":"GZ Li","year":"2009","unstructured":"Li GZ, Meng HH, Yang MQ, Yang JY (2009) Combining support vector regression with feature selection for multivariate calibration. Neural Comput Appl 18:813\u2013820","journal-title":"Neural Computing & Applications"},{"key":"885_CR15","first-page":"315","volume":"36","author":"GZ Li","year":"2006","unstructured":"Li GZ, Yang J, Ye CZ, Geng D (2006) Degree prediction of malignancy in brain glioma using support vector machines. Comput Biol Med 36:315\u2013325","journal-title":"Comput Biol Med"},{"key":"885_CR16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267\u2013288","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"885_CR17","first-page":"418","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou H (2006) The adaptive lasso and its oracle properties. J Am Stat Assoc 101:418\u20131429","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"885_CR18","first-page":"1933","volume":"37","author":"H Zou","year":"2009","unstructured":"Zou H, Zhang HH (2009) On the adaptive elastic net with a diverging number of parameters. Ann Stat 37(4):1933\u20131751","journal-title":"Ann Stat"},{"key":"885_CR19","unstructured":"Friedman J, Hastie T, Tibshirani R (2008) Regularization paths for generalized linear models via coordinate descent. Technical report, Department of Statistics, Standford University, USA"},{"issue":"2","key":"885_CR20","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1214\/07-AOAS131","volume":"1","author":"J Friedman","year":"2007","unstructured":"Friedman J, Hastie T, H\u00f6fling H, Tibshirani R (2007) Pathwise coordinate optimization. Ann Appl Stat 1(2):302\u2013332","journal-title":"Annals of Applied Statistics"},{"key":"885_CR21","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1089\/106652703322756177","volume":"10","author":"M Segal","year":"2003","unstructured":"Segal M, Dahlquist K, Conklin B (2003) Regression approach for microarray data analysis. J Comput Biol 10:961\u2013980","journal-title":"J Comput Biol"},{"key":"885_CR22","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron B, Hastie T, Johnston I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407\u2013451","journal-title":"Ann Stat"},{"key":"885_CR23","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1214\/009053606000001370","volume":"35","author":"S Rosset","year":"2007","unstructured":"Rosset S, Zhu J (2007) Piecewise linear regularized solution paths. Ann Stat 35:1012\u20131030","journal-title":"Ann Stat"},{"key":"885_CR24","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s00521-010-0513-2","volume":"20","author":"C Zheng","year":"2011","unstructured":"Zheng C, Chong Y, wang H (2011) Gene selection using independent variable group analysis for tumor classification. Neural Comput Appl 20:161\u2013170","journal-title":"Neural Comput Appl"},{"key":"885_CR25","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1038\/ng1434","volume":"36","author":"E Segal","year":"2004","unstructured":"Segal E, Friedman N, Koller D, Regev A (2004) A module map showing conditional activity of expression modules in cancer. Nature Genetics 36:1090\u20131098","journal-title":"Nat Genet"},{"key":"885_CR26","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1186\/1471-2105-9-497","volume":"9","author":"MCP Souto de","year":"2008","unstructured":"de Souto MCP, Costa IG, Araujo DAS, Ludermir TB, Schliep A (2008) Clustering cancer gene expression data: a comparative study. BMC Bioinform 9:497","journal-title":"BMC Bioinformatics"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-0885-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-012-0885-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-0885-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T23:44:02Z","timestamp":1713570242000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-012-0885-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2,19]]},"references-count":26,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2013,5]]}},"alternative-id":["885"],"URL":"https:\/\/doi.org\/10.1007\/s00521-012-0885-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,2,19]]}}}