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Activity Number:
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155
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #306985 |
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Title:
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A Statistical Framework To Infer Functional Gene Associations from Multiple Biologically Dependent Microarray Experiments
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Author(s):
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Siew-Leng Teng*+
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Companies:
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University of California, Berkeley
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Address:
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Department of Statistics, Berkeley, CA, 94720-3860,
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Keywords:
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microarray ; functional gene associations ; experimental dependencies ; gene dependencies ; functional gene relationships
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Abstract:
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Microarray data from multiple biologically related experiments now allow for a more complete portrayal of gene dynamics in a biological process. However, a critical issue has been widely ignored in current integrative analyses: existence of dependencies among gene expressions across related experiments. These dependencies can arise either due to similar conditions or relevant external perturbations among the experiments, and can result in inaccurate estimates of gene associations and hence incorrect biological conclusions. To address this issue, we propose a statistical framework and Knorm correlation to quantify functional gene associations in presence of such experimental dependencies. Our model underlines a unique dependency structure that maintains the same experiment (gene) correlations across genes (experiments). Our measure yielded promising results in experimental datasets.
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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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