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dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorWang, Xiaolei
dc.contributor.authorGao, Xin
dc.date.accessioned2014-08-27T09:52:54Z
dc.date.available2014-08-27T09:52:54Z
dc.date.issued2013-03-24
dc.identifier.citationWang JJ-Y, Wang X, Gao X (2013) Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinformatics 14: 107. doi:10.1186/1471-2105-14-107.
dc.identifier.issn14712105
dc.identifier.pmid23522344
dc.identifier.doi10.1186/1471-2105-14-107
dc.identifier.urihttp://hdl.handle.net/10754/325473
dc.description.abstractBackground: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.
dc.language.isoen
dc.publisherSpringer Nature
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subjectExpectation conditional maximizations
dc.subjectGene Expression Data
dc.subjectKullback-Leibler distance
dc.subjectNon-negative matrix
dc.subjectNonnegative matrix factorization
dc.subjectOptimization problems
dc.subjectSimilarity measurements
dc.subjectState-of-the-art methods
dc.subjectDiseases
dc.subjectFilter banks
dc.subjectGene expression
dc.subjectMatrix algebra
dc.subjectFactorization
dc.subjectalgorithm
dc.subjectclassification
dc.subjectcluster analysis
dc.subjectgene expression profiling
dc.subjectgenetics
dc.subjectmetabolism
dc.subjectmethodology
dc.subjectneoplasm
dc.subjectAlgorithms
dc.subjectCluster Analysis
dc.subjectGene Expression Profiling
dc.subjectNeoplasms
dc.titleNon-negative matrix factorization by maximizing correntropy for cancer clustering
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalBMC Bioinformatics
dc.identifier.pmcidPMC3659102
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUnidad Académica de Sistemas Arrecifales (Puerto Morelos), Instituto de Ciencias Del Mar y Limnología, Universidad Nacional Autõnoma de México, Puerto Morelos, QR 77580, Mexico
dc.contributor.institutionSchool of Natural Sciences, University of California Merced, 5200 North Lake Road, Merced, CA 95343, United States
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personWang, Jim Jing-Yan
kaust.personGao, Xin
kaust.personWang, Xiaolei
refterms.dateFOA2018-06-13T15:32:58Z
dc.date.published-online2013-03-24
dc.date.published-print2013


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.