Abstract Details
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Activity Number:
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393
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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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Biometrics Section
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Abstract #313338
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View Presentation
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Title:
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A Comparison of Classification Methods for Metabolic Data
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Author(s):
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Amanda Brucker*+ and Joshua Sampson and Michelle Dunn
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Companies:
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National Cancer Institute and National Cancer Institute and National Cancer Institute
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Keywords:
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classification ;
metabolomics
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Abstract:
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In the field of metabolomics, the standard methods of classification are partial least squares (PLS), partial least squares discriminant analysis (PLS-DA), and their orthogonalized versions (O-PLS, O-PLS-DA). PLS and PLS-DA, and their extensions, are commonly implemented to find patterns of metabolites that can distinguish "case" and "control" groups, treatment groups, cell environments, and other relevant biological classifications. In this project, I compare the PLS method to other traditional statistical methods-including but not limited to ridge regression, lasso, and best subsets-in identifying metabolite profiles that best predict BMI and other health outcomes.
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Authors who are presenting talks have a * after their name.
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