PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discovering temporal patterns of differential gene expression in microarray time series
Oliver Stegle, K Denby, S McHattie, S Meade, David L. Wild, Zoubin Ghahramani and Karsten Borgwardt
In: German Conference on Bioinformatics 2009, 28-30 SEP 2009, Halle, Germany.

Abstract

A wealth of time series of microarray measurements have become available over recent years. Several two-sample tests for detecting differential gene expression in these time series have been defined, but they can only answer the question whether a gene is differentially expressed across the whole time series, not in which intervals it is differentially expressed. In this article, we propose a Gaussian process based approach for studying these dynamics of differential gene expression. In experiments on Arabidopsis thaliana gene expression levels, our novel technique helps us to uncover that the family of WRKY transcription factors appears to be involved in the early response to infection by a fungal pathogen.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6236
Deposited By:Zoubin Ghahramani
Deposited On:08 March 2010