Theses and Dissertations from UMD

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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Wang, Yonghong

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    USING STATISTICAL METHOD TO REVEAL BIOLOGICAL ASPECT OF HUMAN DISEASE: STUDY OF GLIOBLASTOMA BY USING COMPARATIVE GENOMIC HYBRIDIZATION (CGH) METHOD
    (2010) Wang, Yonghong; Smith, Paul; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Glioblastoma 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.