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dc.contributor.advisorFinch, Stephen J.en_US
dc.contributor.authorRoberson, Andreaen_US
dc.contributor.otherDepartment of Applied Mathematics and Statisticsen_US
dc.date.accessioned2012-05-15T18:06:26Z
dc.date.available2012-05-15T18:06:26Z
dc.date.issued1-May-10en_US
dc.date.submittedMay-10en_US
dc.identifierRoberson_grad.sunysb_0771E_10077.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1951/55603
dc.description.abstractArray comparative genomic hybridization (aCGH) can detect copy number variation (CNV) across the genome. Five current Hidden Markov Model (HMM) software systems for estimating copy number variation with aCGH data were compared. These comparisons were in terms of their effectiveness for identifying CNVs in simulated data based on the ratio of signal intensities. There was significant variability in the error rates. The system that adjusted for outliers in the model, the Robust Hidden Markov Model (HMM-R), appeared to have the best performance. The emission density function of the HMM is a mixture of two normal densities, in which one component represents usable aCGH data and the other represents outliers. HMM-R correctly classified 99.8% of normal states, 84.5% of CNV gains, and 90.2% of CNV losses. That is, error rates with regard to gains and losses were appreciable even with the best software. The HMM-R method demonstrated higher sensitivity and lower false discovery rates than the commonly used procedure. While the accuracy rates of HMM software has improved, there is substantial room for further improvement.en_US
dc.description.sponsorshipStony Brook University Libraries. SBU Graduate School in Department of Applied Mathematics and Statistics. Lawrence Martin (Dean of Graduate School).en_US
dc.formatElectronic Resourceen_US
dc.language.isoen_USen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.en_US
dc.subject.lcshApplied Mathematics -- Statisticsen_US
dc.subject.otherBayesian, CNV, Comparison, Hidden, Markov, Modelsen_US
dc.titleA comparison of Hidden Markov Model based programs for detection of copy number variation in array comparative genomic hybridization dataen_US
dc.typeDissertationen_US
dc.description.advisorAdvisor(s): Stephen J. Finch. Committee Member(s): Nancy R. Mendell; Wei Zhu; Derek Gordon.en_US
dc.mimetypeApplication/PDFen_US


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