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Activity Number:
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62
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #306311 |
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Title:
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Clustering of Time-Course Gene Expression Data Using Functional Data Analysis
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Author(s):
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Joon Jin Song*+ and Ho-Jin Lee and Jeffrey S. Morris and Sanghoon Kang
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Companies:
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University of Arkansas and Schering-Plough Corporation and M. D. Anderson Cancer Center and Oak Ridge National Laboratory
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Address:
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Department of Mathematical Sciences, Fayetteville, AR, 72701,
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Keywords:
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time-course microarray ; functional data analysis ; clustering analysis ; principle component analysis
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Abstract:
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Time-course microarray experiments are effective in studying gene expression profile levels over a period of time. Since biological processes are dynamic and complex systems, such characteristics are essential factors in understanding how the underlying mechanisms regulate cellular processes and gene functions. We propose a unified approach for gene clustering and dimension reduction based on Functional Data Analysis to group observed curves with respect to their shapes or patterns by using the sample information in time-course microarray experiments. We apply this method to a time course microarray data set on the yeast cell cycle and a synthetic data set, and demonstrate that our method is able to identify tight clusters of genes with expression profile focused on particular phases of the cell cycle.
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