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Article: Penalized independent component discriminant method for tumor classification

TitlePenalized independent component discriminant method for tumor classification
Authors
KeywordsAlgorithms
Data Reduction
Dna Sequences
Genetic Engineering
Independent Component Analysis
Pattern Recognition
Issue Date2006
PublisherSpringer Verlag
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, v. 4115 LNBI -III, p. 494-503 How to Cite?
AbstractThis paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis (ICA) to model the gene expression data, then apply optimal scoring algorithm to classify them. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. © Springer-Verlag Berlin Heidelberg 2006.
Persistent Identifierhttp://hdl.handle.net/10722/92070
ISSN
2005 Impact Factor: 0.302
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorZheng, C-Hen_HK
dc.contributor.authorShang, Len_HK
dc.contributor.authorChen, Yen_HK
dc.contributor.authorHuang, Z-Ken_HK
dc.date.accessioned2010-09-17T10:35:12Z-
dc.date.available2010-09-17T10:35:12Z-
dc.date.issued2006en_HK
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, v. 4115 LNBI -III, p. 494-503en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/92070-
dc.description.abstractThis paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis (ICA) to model the gene expression data, then apply optimal scoring algorithm to classify them. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. © Springer-Verlag Berlin Heidelberg 2006.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlagen_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.subjectAlgorithmsen_HK
dc.subjectData Reductionen_HK
dc.subjectDna Sequencesen_HK
dc.subjectGenetic Engineeringen_HK
dc.subjectIndependent Component Analysisen_HK
dc.subjectPattern Recognitionen_HK
dc.titlePenalized independent component discriminant method for tumor classificationen_HK
dc.typeArticleen_HK
dc.identifier.emailChen, Y:ychenc@hkucc.hku.hken_HK
dc.identifier.authorityChen, Y=rp1318en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-33749574128en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33749574128&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4115 LNBI -IIIen_HK
dc.identifier.spage494en_HK
dc.identifier.epage503en_HK
dc.identifier.eissn1611-3349-

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