This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307791 |
Title:
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An Empirical Bayes Model for Metabolite Identifications Using Two-Dimensional Gas Chromatography Mass Spectrometry
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Author(s):
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Jaesik Jeong*+ and Changyu Shen and Xiang Zhang
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Companies:
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Indiana University Purdue University Indianapolis and Indiana University Purdue University Indianapolis and University of Louisville
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Address:
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, , 46202,
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Keywords:
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metabolomics ;
expectation-maximization ;
similarity score ;
GCGC/TOF-MS
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Abstract:
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Two dimensional gas chromatography - mass spectrometry (GCxGC/TOF-MS) is an emerging technology that offers significant advantages for the analysis of metabolites present in complex samples. It offers an order-of-magnitude increase in separation capacity over one dimensional gas chromatography, leading to significant improvement in mass spectral de-convolution for metabolite identification through comparing the experimental spectrum with a library of spectra with known identities and assigning the best match. Nevertheless, the identifications of metabolites that generate the large amount of spectra are still subject to errors. Therefore, statistical/computational approaches to improve the accuracy of the identifications and validity of false positive control/estimate are in great need. We propose a hierarchical statistical model in the empirical Bayes framework to tackle this problem.
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Authors who are presenting talks have a * after their name.
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