JSM 2005 - Toronto

Abstract #303940

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 226
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #303940
Title: Gene Discovery and Pattern Recognition for Microarray Time-course Experiments Using Regression Models
Author(s): Hua Liu*+ and Aaron S. Borders and Thomas V. Getchell and Sergey S. Tarima and Marilyn L. Getchell and Arnold J. Stromberg
Companies: University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky
Address: 700 Woodland Ave, Lexington, KY, 40508, United States
Keywords: microarray time-course ; quadratic regression ; pattern recognition ; gene selection
Abstract:

In microarray time-course studies, time dependency of gene expression levels is usually of primary interest. Most commonly used methods treat time as a nominal variable, thus ignoring the actual sampling time. We discuss a quadratic regression method for gene identification and pattern recognition for microarray time-course data. This approach utilizes time information by treating time as a continuous variable. In this method, quadratic/linear regression models are used and gene discovery and pattern classification are determined based on relevant F-statistics and least-squares estimates. This method was applied to a microarray time-course study of olfactory receptor neurons. Biologically meaningful regression patterns have been identified and shown to fit gene expression profiles well. A reliability study based on the bootstrap was used, and regression patterns were shown to have the highest reliabilities when compared with several other methods.

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Revised March 2005