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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2008

Open Access Poster presentation

Gene classification for microarray data with multiple time measurements

Jonathan Quiton1*, Claire Rinehart2, Joseph Chavarria-Smith2 and Nancy Rice2

Author affiliations

1 Department of Mathematics, Western Kentucky University, Bowling Green, KY 42101, USA

2 Department of Biology, Western Kentucky University, Bowling Green, KY 42101, USA

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Citation and License

BMC Bioinformatics 2008, 9(Suppl 7):P18  doi:10.1186/1471-2105-9-S7-P18

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/9/S7/P18


Published:8 July 2008

© 2008 Quiton et al; licensee BioMed Central Ltd.

Background

In microarray data analysis, we considered the problem of classifying genes based on ratio of the mean gene expression levels between the control and the treatment factors measured at t fixed times. In this setting, we assume that the control and treatment responses come from two independent normal populations, and the two treatment groups are significantly different only if the ratio of the two population means is less than r1 or greater than r2. We propose an approach based on the mapping of the T scores into C = {+1, 0, -1}, where +1 is the value when the t-score is greater than the upper critical point, -1 if it is less than the lower critical point, and 0 otherwise. Misclassification probability under small replications is given and the method is demonstrated using a microarray data.