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Activity Number:
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32
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #302499 |
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Title:
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Bayesian Approach for the Identification of DNA Copy Number Changes in aCGH Data
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Author(s):
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Jie Chen*+ and Ayten Yigiter and Kuang-Chao Chang
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Companies:
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University of Missouri-Kansas City and University of Missouri-Kansas City and Fu Jen Catholic University
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Address:
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206 Haag Hall, Kansas City, MO, 64110,
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Keywords:
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Bayesian inferences ; change point ; uninformative priors ; DNA copy numbers ; aCGH data
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Abstract:
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Cancer development or other related diseases are usually relevant to DNA copy number changes on the genome. For the high-throughput array comparative genomic hybridization (aCGH) copy number data, we propose a novel mean and variance change point model (MVCM) for detecting the copy number changes in aCGH data. We use Bayesian approach to study the MVCM for the cases of one change and two changes; and derive the posterior probability for the estimate of the locus or loci of the DNA copy number changes. We carry out simulation studies to evaluate the estimate of the locus (or loci) of the DNA copy number change (or changes) using the derived posterior probability. These simulation results show that the approach is suitable for identifying copy number changes. The approach is successfully applied to the analysis of nine fibroblast cancer cell line data for DNA copy number changes.
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