Show simple item record

dc.contributor.authorMwero, D.K.en_US
dc.date.accessioned2017-02-03T11:04:10Zen_US
dc.date.available2017-02-03T11:04:10Zen_US
dc.date.issued2007en_US
dc.identifier.citationMwero, D. K. 2007. Application of linear mixed models in microarray. MSc thesis in biometry. University of Nairobi.en_US
dc.identifier.urihttps://hdl.handle.net/10568/79638en_US
dc.description.abstractThis project captures the problem of large microarray datasets and seeks to identify a statistical model of microarray hybridization intensity data that describes;differential regulation, sample variability and measurement noise. It also shows how one can use the data model to analyze the microarray data and develop optimal methods for detecting differentially regulated peripheral blood leukocyte mRNA from cattle infected with Trypanosoma congolense using microarray in order to assay components of the immune and inflammatory responses and identify potential correlates of the pathology. We conclude by giving an insight into linear mixed effects models by analysing a data set from a cattle experiment that seeks to compare 'genome-wide' transcriptional responses in blood leukocytes following infection with species of Trypanosoma that differ in the severity of pathogenicity.en_US
dc.language.isoenen_US
dc.publisherUniversity of Nairobien_US
dc.subjectTRYPANOSOMAen_US
dc.subjectPATHOLOGYen_US
dc.subjectGENOMESen_US
dc.titleApplication of linear mixed models in microarray.en_US
dc.typeThesisen_US
cg.subject.ilriRESEARCHen_US
cg.subject.ilriTRYPANOSOMIASISen_US
cg.identifier.statusLimited Accessen_US
cg.contributor.affiliationUniversity of Nairobien_US
cg.targetaudienceACADEMICSen_US
cg.fulltextstatusGrey Literatureen_US
cg.identifier.urlhttp://erepository.uonbi.ac.ke/handle/11295/19724en_US
cg.placeNairobi, Kenyaen_US
cg.coverage.regionAFRICAen_US
cg.coverage.regionEAST AFRICAen_US
cg.coverage.countryKENYAen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record