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USING STATISTICAL METHOD TO REVEAL BIOLOGICAL ASPECT OF HUMAN DISEASE: STUDY OF GLIOBLASTOMA BY USING COMPARATIVE GENOMIC HYBRIDIZATION (CGH) METHOD

dc.contributor.advisorSmith, Paulen_US
dc.contributor.authorWang, Yonghongen_US
dc.date.accessioned2010-10-07T06:11:52Z
dc.date.available2010-10-07T06:11:52Z
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1903/10950
dc.description.abstractGlioblastoma is a WHO grade IV tumor with high mortality rate. In order to identify the underlying biological causation of this disease, a comparative genomic hybridization dataset generated from 170 patients' tumor samples was analyzed. Of many available segmentation algorithms, I focused mainly on two most acceptable methods: Homogeneous Hidden Markov Models (HHMM) and Circular Binary Segmentation (CBS). Simulations show that CBS tends to give better segmentation result with low false discovery rate. HHMM failed to identify many obvious breakpoints that CBS identified. On the other hand, HHMM succeeds in identifying many single probe aberrations. Applying other statistical algorithms revealed distinct biological fingerprints of Glioblastoma disease, which includes many signature genes and biological pathways. Survival analysis also reveals that several segments actually correlate to the extended survival time of some patients. In summary, this work shows the importance of statistical model or algorithms in the modern genomic research.en_US
dc.titleUSING STATISTICAL METHOD TO REVEAL BIOLOGICAL ASPECT OF HUMAN DISEASE: STUDY OF GLIOBLASTOMA BY USING COMPARATIVE GENOMIC HYBRIDIZATION (CGH) METHODen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMathematical Statisticsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledBiology, Biostatisticsen_US
dc.subject.pquncontrolledbiologyen_US
dc.subject.pquncontrolledCGHen_US
dc.subject.pquncontrolledGlioblastomaen_US
dc.subject.pquncontrolledsegmentationen_US
dc.subject.pquncontrolledStatisticsen_US
dc.subject.pquncontrolledTCGAen_US


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