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Activity Number:
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142
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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WNAR
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| Abstract - #309858 |
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Title:
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Network-Based Meta Analysis Methods for Microarray Data
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Author(s):
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Wen Lin*+ and Steve Horvath
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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29 Alevera St, Irvine, CA, 92618,
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Keywords:
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network
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Abstract:
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The accumulation of microarray data poses a new statistical problem: how to combine multiple gene expression data that have been measured using different technological platforms and different measurement protocols. Toward this end, several meta-analysis techniques have been suggested in the literature. Since traditional meta analysis techniques may ignore important biological pathway, we propose a novel method that makes use of weighted gene co-expression network concepts. Specifically, we propose two types of network-based scores that allow one to rank genes based on their gene significance levels and module membership information. Using simulations and real data applications, we compare the performance of these network based meta analysis techniques to several existing approaches including a recent method based on the concept of "integrative correlation" (Parmigiani et al. 2004).
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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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