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Conference Paper: A graph-based elastic net for variable selection and module identification for genomic data analysis

TitleA graph-based elastic net for variable selection and module identification for genomic data analysis
Authors
KeywordsElastic net
Laplacian graph
Pathway
Issue Date2010
PublisherIEEE.
Citation
The 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China, 18-21 December 2010. In Proceedings of the IEEE-BIBM, 2010, p. 357-362 How to Cite?
AbstractRecently a network-constraint regression model[1] is proposed to incorporate the prior biological knowledge to perform regression and variable selection. In their method, a l 1-norm of the coefficients is defined to impose sparse, meanwhile a Laplacian operation on the biological graph is designed to encourage smoothness of the coefficients along the network. However the grouping effect of their Laplacian smoothness operation only exits when the two connected genes both have positive or negative effects on the response. To overcome this problem, we proposed to apply the Laplacian operation on the absolute values of the coefficients to take account of the positive and negative effects. Here, we call the presented method as graph-based elastic net (GENet) because the proposed method has similar grouping effect with elastic net(ENet)[2] except the smoothness of two coefficients are specified by the network in GENet. Further, an efficient algorithm which has same spirit with LARS [3] is developed to solve our optimization problem. Simulation studies showed that the proposed method has better performance than network-constrained regularization without absolute values. Application to Alzheimer's disease(AD) microarray gene-expression dataset identified several subnetworks on Kyoto Encyclopedia of Genes and Genomes(KEGG) transcriptional pathways that are related to progression of AD. Many of those findings are confirmed by published literatures. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140295
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorXia, Zen_HK
dc.contributor.authorZhou, Xen_HK
dc.contributor.authorChen, Wen_HK
dc.contributor.authorChang, Cen_HK
dc.date.accessioned2011-09-23T06:09:40Z-
dc.date.available2011-09-23T06:09:40Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China, 18-21 December 2010. In Proceedings of the IEEE-BIBM, 2010, p. 357-362en_HK
dc.identifier.isbn978-1-4244-8305-1-
dc.identifier.urihttp://hdl.handle.net/10722/140295-
dc.description.abstractRecently a network-constraint regression model[1] is proposed to incorporate the prior biological knowledge to perform regression and variable selection. In their method, a l 1-norm of the coefficients is defined to impose sparse, meanwhile a Laplacian operation on the biological graph is designed to encourage smoothness of the coefficients along the network. However the grouping effect of their Laplacian smoothness operation only exits when the two connected genes both have positive or negative effects on the response. To overcome this problem, we proposed to apply the Laplacian operation on the absolute values of the coefficients to take account of the positive and negative effects. Here, we call the presented method as graph-based elastic net (GENet) because the proposed method has similar grouping effect with elastic net(ENet)[2] except the smoothness of two coefficients are specified by the network in GENet. Further, an efficient algorithm which has same spirit with LARS [3] is developed to solve our optimization problem. Simulation studies showed that the proposed method has better performance than network-constrained regularization without absolute values. Application to Alzheimer's disease(AD) microarray gene-expression dataset identified several subnetworks on Kyoto Encyclopedia of Genes and Genomes(KEGG) transcriptional pathways that are related to progression of AD. Many of those findings are confirmed by published literatures. ©2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartofProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsProceedings of the IEEE International Conference on Bioinformatics and Biomedicine. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectElastic neten_HK
dc.subjectLaplacian graphen_HK
dc.subjectPathwayen_HK
dc.titleA graph-based elastic net for variable selection and module identification for genomic data analysisen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-8305-1&volume=&spage=357&epage=362&date=2010&atitle=A+graph-based+elastic+net+for+variable+selection+and+module+identification+for+genomic+data+analysis-
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BIBM.2010.5706591en_HK
dc.identifier.scopuseid_2-s2.0-79952385422en_HK
dc.identifier.hkuros196547en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952385422&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage357en_HK
dc.identifier.epage362en_HK
dc.description.otherThe 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China, 18-21 December 2010. In Proceedings of the IEEE-BIBM, 2010, p. 357-362-
dc.identifier.scopusauthoridXia, Z=16235569100en_HK
dc.identifier.scopusauthoridZhou, X=8914487400en_HK
dc.identifier.scopusauthoridChen, W=37013075600en_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK

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