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Abstract Details
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Activity Number:
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480
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #302527 |
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Title:
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Modeling Long Time-Series Gene Expression Data with the Kolmogorov-Zurbenko Algorithm
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Author(s):
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Darlene M. Olsen*+
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Companies:
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Norwich University
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Address:
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158 Harmon Drive, Northfield, VT, 05663,
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Keywords:
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micorarray ;
time series ;
spline
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Abstract:
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Long time-series microarray analysis offers an exciting opportunity in biomedical research to investigate the progression of temporal gene expression profiles after certain intervention or treatment, which can yield a more accurate assessment of how altered cellular pathways may interact over time and give insight to the progression of diseases. The Kolmogorov-Zurbenko algorithm uses the Kolmogorov-Zurbenko Spline (KZS) as the modeling framework for temporal profiles and clusters genes based on the parameters of the KZS function. Applying this statistical methodology to analyze time-series microarray data will improve our cognizance of biological networks.
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