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Article: Sparse logistic regression with Lp penalty for biomarker identification

TitleSparse logistic regression with Lp penalty for biomarker identification
Authors
KeywordsFeature Selection
Lp Penalty
Microarry Analysis
Sparse Logistic Regression
Issue Date2007
PublisherBerkeley Electronic Press. The Journal's web site is located at http://www.bepress.com/sagmb
Citation
Statistical Applications In Genetics And Molecular Biology, 2007, v. 6 n. 1 How to Cite?
AbstractIn this paper, we propose a novel method for sparse logistic regression with non-convex regularization Lp (p <1). Based on smooth approximation, we develop several fast algorithms for learning the classifier that is applicable to high dimensional dataset such as gene expression. To the best of our knowledge, these are the first algorithms to perform sparse logistic regression with an Lp and elastic net (Le) penalty. The regularization parameters are decided through maximizing the area under the ROC curve (AUC) of the test data. Experimental results on methylation and microarray data attest the accuracy, sparsity, and efficiency of the proposed algorithms. Biomarkers identified with our methods are compared with that in the literature. Our computational results show that Lp Logistic regression (p <1) outperforms the L1 logistic regression and SCAD SVM. Software is available upon request from the first author. Copyright ©2007 The Berkeley Electronic Press. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/172429
ISSN
2011 Impact Factor: 1.517
2015 SCImago Journal Rankings: 0.954
References

 

DC FieldValueLanguage
dc.contributor.authorLiu, Zen_US
dc.contributor.authorJiang, Fen_US
dc.contributor.authorTian, Gen_US
dc.contributor.authorWang, Sen_US
dc.contributor.authorSato, Fen_US
dc.contributor.authorMeltzer, SJen_US
dc.contributor.authorTan, Men_US
dc.date.accessioned2012-10-30T06:22:29Z-
dc.date.available2012-10-30T06:22:29Z-
dc.date.issued2007en_US
dc.identifier.citationStatistical Applications In Genetics And Molecular Biology, 2007, v. 6 n. 1en_US
dc.identifier.issn1544-6115en_US
dc.identifier.urihttp://hdl.handle.net/10722/172429-
dc.description.abstractIn this paper, we propose a novel method for sparse logistic regression with non-convex regularization Lp (p <1). Based on smooth approximation, we develop several fast algorithms for learning the classifier that is applicable to high dimensional dataset such as gene expression. To the best of our knowledge, these are the first algorithms to perform sparse logistic regression with an Lp and elastic net (Le) penalty. The regularization parameters are decided through maximizing the area under the ROC curve (AUC) of the test data. Experimental results on methylation and microarray data attest the accuracy, sparsity, and efficiency of the proposed algorithms. Biomarkers identified with our methods are compared with that in the literature. Our computational results show that Lp Logistic regression (p <1) outperforms the L1 logistic regression and SCAD SVM. Software is available upon request from the first author. Copyright ©2007 The Berkeley Electronic Press. All rights reserved.en_US
dc.languageengen_US
dc.publisherBerkeley Electronic Press. The Journal's web site is located at http://www.bepress.com/sagmben_US
dc.relation.ispartofStatistical Applications in Genetics and Molecular Biologyen_US
dc.subjectFeature Selectionen_US
dc.subjectLp Penaltyen_US
dc.subjectMicroarry Analysisen_US
dc.subjectSparse Logistic Regressionen_US
dc.titleSparse logistic regression with Lp penalty for biomarker identificationen_US
dc.typeArticleen_US
dc.identifier.emailTian, G: gltian@hku.hken_US
dc.identifier.authorityTian, G=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-33847007697en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33847007697&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume6en_US
dc.identifier.issue1en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLiu, Z=35327344500en_US
dc.identifier.scopusauthoridJiang, F=35210144000en_US
dc.identifier.scopusauthoridTian, G=25621549400en_US
dc.identifier.scopusauthoridWang, S=53870935700en_US
dc.identifier.scopusauthoridSato, F=7402130614en_US
dc.identifier.scopusauthoridMeltzer, SJ=7102844146en_US
dc.identifier.scopusauthoridTan, M=7401464681en_US

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