Abstract Details
Activity Number:
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145
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #307733 |
Title:
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Addressing Within-Subject Genomic Heterogeneity
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Author(s):
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Matthew Nicholson McCall*+ and Anthony Almudevar
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Companies:
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University of Rochester Medical Center and University of Rochester Medical Center
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Keywords:
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genomics ;
cancer ;
heterogeneity ;
extrema ;
biomarker
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Abstract:
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Tissue biopsy followed by histopathology, or more recently genomic analysis, is a standard procedure to assess the type, severity, and prognosis of many diseases, particularly cancer. Within-subject genomic heterogeneity, in which a single patient displays vastly different genomic profiles between samples, represents a challenge to such analysis. Here we show that in the presence of within-subject heterogeneity, an extremum statistic may outperform a measure of centeral tendency in capturing the genomic signature of interest. We use a simple heterogeneity model to explore situations in which the mean is outperformed by an extremum and demonstrate the applicability of this approach on a hepatocellular carcinoma data set.
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Authors who are presenting talks have a * after their name.
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