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Activity Number:
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113
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #307011 |
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Title:
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Incorporation of Metabolic Insight into Analysis of High-Dimensional Structural Lipid Datasets
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Author(s):
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Michelle Wiest*+ and UyenThao Nguyen and Aldo Bernasconi
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Companies:
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Lipomics Technologies, Inc. and Lipomics Technologies, Inc. and Lipomics Technologies, Inc.
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Address:
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P.O. Box 593, Knights Landing, CA, 95645,
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Keywords:
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metabolomics ; high-throughput ; lipids
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Abstract:
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The emerging field of Metabolomics promises to diagnose and guide human health. The failure of first-generation diagnostics to reach clinical practice derives from the use of datasets where properties of metabolites were measured but the metabolites were not identified. While valuable for screening, such platforms are unable to identify metabolic pathways that cause subsequent health problems. Quantitative platforms focusing on known metabolites can resolve statistically robust and biologically meaningful differences. The statistical challenge is combining analytical precision and biological accuracy into the modeling of high density metabolite datasets. Examples of accurate, comprehensive measurements of certain known metabolites (structural lipids) show the power of biologically supervised statistical estimation to create actionable diagnostics suitable for personal health assessment.
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