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Bayesian non-linear matching of pairwise microarray gene expressions

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorMarín Díazaraque, Juan Miguel
dc.contributor.authorNieto, Carmen
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2008-05-28T09:45:57Z
dc.date.available2008-05-28T09:45:57Z
dc.date.issued2008-05
dc.description.abstractIn this paper, we present a Bayesian non-linear model to analyze matching pairs of microarray expression data. This model generalizes, in terms of neural networks, standard linear matching models. As a practical application, we analyze data of patients with Acute Lymphoblastic Leukemia and we find out the best neural net model that relates the expression levels of two types of cytogenetically different samples from them.
dc.format.mimetypeapplication/pdf
dc.identifier.repecws082507
dc.identifier.urihttps://hdl.handle.net/10016/2611
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries08-07
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherMCMC computation
dc.subject.otherMicroarray data analysis
dc.subject.otherMultidimensional scaling
dc.subject.otherNeural network
dc.subject.otherSpatial Bayesian methods
dc.titleBayesian non-linear matching of pairwise microarray gene expressions
dc.typeworking paper*
dspace.entity.typePublication
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