Abstract Details
Activity Number:
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391
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Korean International Statistical Society
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Abstract #311145
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View Presentation
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Title:
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Hierarchical Peak Detection Algorithms for Comprehensive Two-Dimensional Gas Chromatography Time-Of-Flight Mass Spectrometry Data
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Author(s):
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Seongho Kim*+ and Xiang Zhang and Changyu Shen
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Companies:
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Wayne State University/Karmanos Cancer Institute and University of Louisville and Indiana University
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Keywords:
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Hierarchical models ;
Peak detection ;
Mass spectrometry ;
Bayes factors ;
Exponentially modified Gaussian ;
Mixture models
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Abstract:
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A novel hierarchical peak detection algorithm is developed for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm simultaneously performs baseline correction and de-noising under NEB models and then detects peaks using a mixture of probability distribution to deal with the co-eluting peaks. After that, peak merging is rendered using the mass spectral similarities. The new algorithm was further compared with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used exponentially modified Gaussian (EMG) mixture models.
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Authors who are presenting talks have a * after their name.
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