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Activity Number:
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143
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Type:
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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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Biometrics Section
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| Abstract - #308452 |
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Title:
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A Method of Detecting Differential Gene Expression for Cross-Species Hybridization Experiments
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Author(s):
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Ying Chen*+ and Anu Chakicherla and David Rocke
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Companies:
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University of California, Davis and Lawrence Livermore National Laboratory and University of California, Davis
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Address:
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4400 Solano Park Circle, Davis, CA, 95616,
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Keywords:
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cross-species ; microarray ; gene set analysis ; GSEA ; ToTS
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Abstract:
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Cross-species microarray hybridization has been shown to be a potentially powerful tool for understanding genomics in organisms in which there is no complete genomic sequence available. However, sequence differences between two species made it difficult to detect any significant change in gene expressions. Previous literature demonstrated an empirical masking procedure to analyze such data. The fact that gene set analysis is useful to detect minimal or moderate changes in gene expressions motivated us to apply this technique in cross-species analysis. In this study, we proposed a novel knowledge-based method to analyze a real cross-species data set. We compared the results of two gene set analysis tools (GSEA and ToTS) used in this data set. Our findings showed that gene set analysis method can be used as a powerful technique for analyzing cross-species hybridization experiments.
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