JSM 2011 Online Program

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Abstract Details

Activity Number: 480
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #302527
Title: Modeling Long Time-Series Gene Expression Data with the Kolmogorov-Zurbenko Algorithm
Author(s): Darlene M. Olsen*+
Companies: Norwich University
Address: 158 Harmon Drive, Northfield, VT, 05663,
Keywords: micorarray ; time series ; spline

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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