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Phylogenetic Approaches to Microbial Community Classification

dc.contributor.authorNing, Jie
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorEvangelos Miliosen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerHong Guen_US
dc.contributor.thesis-readerThomas Trappenbergen_US
dc.contributor.thesis-supervisorRobert G. Beikoen_US
dc.date.accessioned2015-08-18T16:44:20Z
dc.date.available2015-08-18T16:44:20Z
dc.date.defence2015-08-07
dc.date.issued2015
dc.description.abstractThe microorganisms associated with our body, collectively known as the microbiome, have profound impacts on biological processes including human health and disease. Different body sites are dominated by different major groups of microbes, but the variations within a body site, such as the mouth, can be more subtle. High-throughput DNA sequencing allows the assessment of the microbiome at an unprecedented scale, but creates new computational challenges. Machine-learning procedures can serve as useful tools for distinguishing microbes from similar body sites, understanding key organisms and their roles can highlight deviations from expected distributions of microbes.en_US
dc.identifier.urihttp://hdl.handle.net/10222/60452
dc.language.isoen_USen_US
dc.subjectSupervised-learningen_US
dc.subjectmicrobiomeen_US
dc.titlePhylogenetic Approaches to Microbial Community Classificationen_US
dc.typeThesisen_US

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