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  A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series

Stegle, O., Denby, K., Cooke, E., Wild, D., Ghahramani, Z., & Borgwardt, K. (2010). A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. Journal of Computational Biology, 17(3), 355-367. doi:10.1089/cmb.2009.0175.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C010-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-7657-8
Genre: Journal Article

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 Creators:
Stegle, O1, 2, Author              
Denby, KJ, Author
Cooke, EJ, Author
Wild, DL, Author
Ghahramani, Z, Author
Borgwardt, KM1, 2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              

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 Abstract: Understanding the regulatory mechanisms that are responsible for an organism‘s response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.

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 Dates: 2010-05
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1089/cmb.2009.0175
BibTex Citekey: 6525
 Degree: -

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Title: Journal of Computational Biology
Source Genre: Journal
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Publ. Info: New York, NY : Mary Ann Liebert
Pages: - Volume / Issue: 17 (3) Sequence Number: - Start / End Page: 355 - 367 Identifier: ISSN: 1066-5277
CoNE: https://pure.mpg.de/cone/journals/resource/954925275499