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Activity Number:
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379
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304724 |
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Title:
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Biomarker Detection Methods When Combining Multiple Multi-Class Microarray Studies
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Author(s):
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Shu-Ya Lu*+
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Companies:
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University of Pittsburgh
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Address:
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3407 Ward Street, Pittsburgh, PA, 15213,
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Keywords:
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Microarray; ; Meta-analysis ; Multi-class data; ; patterns
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Abstract:
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As the microarray technology becomes mature and prevalent in biomedical research, increasing number of data sets has been accumulated in the public domain. Systematic information integration of multiple related studies can provide improved biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and pattern concordance are rarely explored. We first consider a natural extension of combining p-values from the traditional ANOVA model. Since p-values from ANOVA do not guarantee to reflect the information of concordant expression pattern within classes, we propose a multi-class correlation measure (MCC) to specifically seek for biomarkers of concordant inter-class patterns across a pair of studies.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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