Comparative evaluation of different graphical models for the analysis of gene expression data

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dc.contributor.advisorUrfer, Wolfgang
dc.contributor.authorGrzegorczyk, Marco
dc.contributor.refereeWeihs, Claus
dc.date.accepted2006-08-24
dc.date.accessioned2006-08-28T09:24:50Z
dc.date.available2006-08-28T09:24:50Z
dc.date.issued2006-08-28T09:24:50Z
dc.description.abstractAn important problem in systems biology is to infer the architecture of gene regulatory networks and biochemical pathways from postgenomic data. Various reverse engineering methods have been developed and proposed in the Statistics and Bioinformatics literature, and it is important to understand their relative merits and shortcomings. To shed light onto this problem, the learning performances of three widely-used Machine Learning methodologies: Relevance Networks, Graphical Gaussian models, and Bayesian Networks are evaluated and compared on different real and synthetic test data sets taken from the RAF signalling network which describes the interactions between eleven phosphorylated proteins and phospholipids in human immune system cells.en
dc.format.extent1453627 bytes
dc.format.extent13464 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2003/22855
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-607
dc.identifier.urnurn:nbn:de:hbz:290-2003/22855-7
dc.language.isoen
dc.subjectGene regulatory networksen
dc.subjectBayesian networksen
dc.subjectGraphical Gaussian modelsen
dc.subjectRelevance networksen
dc.subject.ddc310
dc.titleComparative evaluation of different graphical models for the analysis of gene expression dataen
dc.typeTextde
dc.type.publicationtypedoctoralThesis
dcterms.accessRightsopen access

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Fachgebiet Statistische Methoden in der Genetik und Ökologie