{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:22:48Z","timestamp":1765545768637},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"24","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target.<\/jats:p>\n               <jats:p>Results: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.<\/jats:p>\n               <jats:p>Availability: By request from the author.<\/jats:p>\n               <jats:p>Contact: \u00a0pyz@dblab.chungbuk.ac.kr; khryu@dblab.cbnu.ac.kr<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts602","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T00:24:35Z","timestamp":1350001475000},"page":"3306-3315","source":"Crossref","is-referenced-by-count":75,"title":["An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data"],"prefix":"10.1093","volume":"28","author":[{"given":"Yongjun","family":"Piao","sequence":"first","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea and 2Division of Bio-Medical informatics, Center for Genome Science, Korea National Institute of Health, Osong, South Korea"}]},{"given":"Minghao","family":"Piao","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea and 2Division of Bio-Medical informatics, Center for Genome Science, Korea National Institute of Health, Osong, South Korea"}]},{"given":"Kiejung","family":"Park","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea and 2Division of Bio-Medical informatics, Center for Genome Science, Korea National Institute of Health, Osong, South Korea"}]},{"given":"Keun Ho","family":"Ryu","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea and 2Division of Bio-Medical informatics, Center for Genome Science, Korea National Institute of Health, Osong, South Korea"}]}],"member":"286","published-online":{"date-parts":[[2012,10,11]]},"reference":[{"key":"2023012513244166800_bts602-B1","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1093\/bioinformatics\/btp630","article-title":"Robust biomarker identification for cancer diagnosis with ensemble feature selection methods","volume":"26","author":"Abeel","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B2","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1016\/j.patcog.2008.10.023","article-title":"Model selection for the LS-SVM. Application to handwriting recognition","volume":"42","author":"Adankon","year":"2009","journal-title":"Pattern Recognit."},{"key":"2023012513244166800_bts602-B3","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis","volume":"36","author":"Akay","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"2023012513244166800_bts602-B4","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023012513244166800_bts602-B5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/ng765","article-title":"MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia","volume":"30","author":"Armstrong","year":"2002","journal-title":"Nat. Genet."},{"key":"2023012513244166800_bts602-B6","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/JPROC.2002.804682","article-title":"Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features","volume":"90","author":"Cho","year":"2002","journal-title":"Proc. IEEE"},{"key":"2023012513244166800_bts602-B7","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10489-006-0020-4","article-title":"Cancer classification using ensemble of neural networks with multiple significant gene subsets","volume":"26","author":"Cho","year":"2007","journal-title":"Appl. Intell."},{"key":"2023012513244166800_bts602-B8","doi-asserted-by":"crossref","DOI":"10.1109\/ICCIMA.2007.288","article-title":"Efficient dimensionality reduction approaches for feature selection","volume-title":"International Conference on Computational Intelligence and Multimedia Applications","author":"Deisy","year":"2007"},{"key":"2023012513244166800_bts602-B9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1471-2105-7-3","article-title":"Gene selection and classification of microarray data using random forest","volume":"7","author":"D\u00edaz-Uriarteb","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023012513244166800_bts602-B51","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1093\/bioinformatics\/btf867","article-title":"Boosting for tumor classification with gene expression data","volume":"19","author":"Dettling","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B10","article-title":"An evaluation of feature selection methods and their application to computer security","volume-title":"Technical report","author":"Doak","year":"1992"},{"key":"2023012513244166800_bts602-B11","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1186\/1471-2105-8-267","article-title":"Classification of heterogeneous microarray data by maximum entropy kernel","volume":"8","author":"Fujibuchi","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023012513244166800_bts602-B12","first-page":"3","article-title":"Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profile","volume":"2","author":"George","year":"2011","journal-title":"Int. J. Comput. Sci. Eng. Surv."},{"key":"2023012513244166800_bts602-B13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.patcog.2009.06.009","article-title":"Feature subset selection in large dimensionality domains","volume":"43","author":"Gheyas","year":"2010","journal-title":"Pattern Recognit."},{"key":"2023012513244166800_bts602-B14","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: class discovery and class prediction by gene expression monitoring","volume":"286","author":"Golub","year":"1999","journal-title":"Science"},{"key":"2023012513244166800_bts602-B50","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Machine. Learn."},{"key":"2023012513244166800_bts602-B15","first-page":"339","article-title":"Attribute-oriented induction in data mining","volume-title":"Advances in Knowledge Discovery sand Data Mining","author":"Han","year":"1996"},{"key":"2023012513244166800_bts602-B16","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1162\/neco.2007.09-06-340","article-title":"Brain reading using full brain support vector machines for object recognition: there is no \u2018face\u2019 identification area","volume":"20","author":"Hanson","year":"2008","journal-title":"Neural Comput."},{"key":"2023012513244166800_bts602-B17","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1214\/aos\/1028144844","article-title":"Classification by pairwise coupling","volume":"26","author":"Hastie","year":"1998","journal-title":"Ann. Statist."},{"key":"2023012513244166800_bts602-B18","volume-title":"A Practical Guide to Support Vector Classification","author":"Hsu","year":"2010"},{"key":"2023012513244166800_bts602-B19","doi-asserted-by":"crossref","first-page":"8144","DOI":"10.1016\/j.eswa.2010.12.156","article-title":"Hybrid feature selection by combining filters and wrappers","volume":"38","author":"Hsu","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"2023012513244166800_bts602-B52","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/1471-2105-6-148","article-title":"Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes","volume":"6","author":"Jirapech-Umpai","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2023012513244166800_bts602-B20","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.knosys.2010.03.016","article-title":"A novel hybrid feature selection via symmetrical uncertainty ranking based local memetric search algorithm","volume":"23","author":"Kannan","year":"2010","journal-title":"Knowl. Based Syst."},{"key":"2023012513244166800_bts602-B21","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1145\/347090.347169","article-title":"Feature selection for unsupervised learning via evolutionary search","volume-title":"Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA","author":"Kim","year":"2000"},{"key":"2023012513244166800_bts602-B22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"2023012513244166800_bts602-B23","first-page":"77","article-title":"An extensive comparison of recent classification tools applied to microarray data","volume":"48","author":"Lee","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"2023012513244166800_bts602-B24","doi-asserted-by":"crossref","first-page":"3793","DOI":"10.1002\/pmic.201100189","article-title":"QSE: a new 3-D solvent exposure measure for the analysis of protein structure","volume":"11","author":"Li","year":"2011","journal-title":"Proteomics"},{"key":"2023012513244166800_bts602-B25","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1093\/bioinformatics\/bth267","article-title":"A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression","volume":"20","author":"Li","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B26","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1016\/j.patcog.2010.02.008","article-title":"Ensemble gene selection for cancer classification","volume":"43","author":"Liu","year":"2010","journal-title":"Pattern Recognit."},{"key":"2023012513244166800_bts602-B27","first-page":"395","article-title":"Feature selection with selective sampling","volume-title":"Proceedings of the Nineteenth International Conference on Machine Learning, 2002","author":"Liu","year":"2002"},{"key":"2023012513244166800_bts602-B28","first-page":"319","article-title":"A probabilistic approach to feature selection\u2014a filter solution","volume-title":"Proceedings of the Thirteenth International Conference on Machine Learning. Bari, Italy","author":"Liu","year":"1996"},{"key":"2023012513244166800_bts602-B29","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","article-title":"Toward integrating feature selection algorithms for classification and clustering","volume":"17","author":"Liu","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2023012513244166800_bts602-B30","doi-asserted-by":"crossref","first-page":"4356","DOI":"10.1093\/bioinformatics\/bti724","article-title":"Regularized ROC method for disease classification and biomarker selection with microarray data","volume":"21","author":"Ma","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/415436a","article-title":"Prediction of central nervous system embryonal tumour outcome based on gene expression","volume":"415","author":"Pomeroy","year":"2002","journal-title":"Nature"},{"key":"2023012513244166800_bts602-B32","doi-asserted-by":"crossref","first-page":"385","DOI":"10.6026\/97320630004385","article-title":"Effective feature selection framework for cluster analysis of microarray data","volume":"4","author":"Pok","year":"2010","journal-title":"Bioinformation"},{"key":"2023012513244166800_bts602-B33","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S1535-6108(02)00030-2","article-title":"Gene expression correlates of clinical prostate cancer behavior","volume":"2","author":"Singh","year":"2002","journal-title":"Cancer Cell"},{"key":"2023012513244166800_bts602-B34","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1093\/bioinformatics\/bti033","article-title":"A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis","volume":"21","author":"Statnikov","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B35","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1186\/1471-2105-9-319","article-title":"A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification","volume":"9","author":"Statnikov","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023012513244166800_bts602-B36","first-page":"3583","article-title":"Ensemble machine learning on gene expression data for cancer classification","volume":"20","author":"Tan","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B37","first-page":"440","article-title":"An evaluation of filter and wrapper methods for feature selection in categorical clustering","volume-title":"Proceedings of 6th International Symposium on Intelligent Data Analysis","author":"Talavera","year":"2005"},{"key":"2023012513244166800_bts602-B38","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik","year":"1995"},{"key":"2023012513244166800_bts602-B39","doi-asserted-by":"crossref","first-page":"11462","DOI":"10.1073\/pnas.201162998","article-title":"Predicting the clinical status of human breast cancer by using gene expression profiles","volume":"98","author":"West","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012513244166800_bts602-B40","doi-asserted-by":"crossref","first-page":"5809","DOI":"10.1016\/j.eswa.2010.10.050","article-title":"Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases","volume":"38","author":"Xie","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"2023012513244166800_bts602-B41","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1006\/mgme.2001.3193","article-title":"Feature (Gene) selection in gene expression-based tumor classification","volume":"73","author":"Xiong","year":"2001","journal-title":"Mol. Genet. Metab."},{"key":"2023012513244166800_bts602-B42","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1186\/1471-2105-7-228","article-title":"A stable gene selection in microarray data analysis","volume":"7","author":"Yang","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023012513244166800_bts602-B43","first-page":"23","article-title":"IG-GA: a hybrid filter\/wrapper method for feature selection of microarray data","volume":"30","author":"Yang","year":"2009","journal-title":"J. Med. Biol. Eng."},{"key":"2023012513244166800_bts602-B44","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01969720802188292","article-title":"Applying data mining techniques for cancer classification on gene expression data","volume":"39","author":"Yeh","year":"2008","journal-title":"Cybern. Syst. Int. J."},{"key":"2023012513244166800_bts602-B45","first-page":"856","article-title":"Feature selection for high-dimensional data: a fast correlation-based filter solution","volume-title":"Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003)","author":"Yu","year":"2003"},{"key":"2023012513244166800_bts602-B46","first-page":"1205","article-title":"Efficient feature selection via analysis of relevance and redundancy","volume":"5","author":"Yu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"2023012513244166800_bts602-B47","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"2023012513244166800_bts602-B48","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1108\/02635570910957669","article-title":"Text classification: neural networks vs support vector machines","volume":"109","author":"Zaghloul","year":"2009","journal-title":"Ind. Manag. Data Syst."},{"key":"2023012513244166800_bts602-B49","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.jtbi.2009.03.025","article-title":"A novel representation for apoptosis protein subcellular localization prediction using support vector machine","volume":"259","author":"Zhang","year":"2009","journal-title":"J. Theor. Biol."}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/24\/3306\/48878661\/bioinformatics_28_24_3306.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/24\/3306\/48878661\/bioinformatics_28_24_3306.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T19:22:01Z","timestamp":1674674521000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/28\/24\/3306\/245457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,11]]},"references-count":52,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2012,12,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bts602","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2012,12]]},"published":{"date-parts":[[2012,10,11]]}}}