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OJBTM

 Online Journal of Bioinformatics  

Volume 11 (1): 145-160, 2010.


Statistical approach to assign functional regulators in gene expression compendia.

 

Forcheh Chiara Anyiawung1, Geert Verbeke1;2 Dirk Valkenborg1;2;4, Hui Zhao3,

Kathleen Marchal3, and Kristof Engelen3

 

1Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35, Blok D, bus 7001, B3000 Leuven, 2Interuniversity Institute for Biostatistics and statistical Bioinformatics,Universiteit Hasselt, Agoralaan 1, B3590 Diepenbeek, 3Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Leuven, 4Flemish Institute for Technological Research (VITO), Boeretang 200, B2400, Mol, Belgium


ABSTRACT

 

Anyiawung FC, Verbeke G Valkenborg D, Zhao H, Marchal K, Engelen K, Statistical approach to assign functional regulators in gene expression compendia, Onl J Bioinform, 11 (1): 145-160, 2010. Current developments in high-throughput techniques for life-sciences, such as microarray technology, have resulted in a rapid accumulation of expression datasets for many types of organisms. Obtaining the rich biological information from such data sets is challenging. In order to address this challenge, we present a novel approach for assessing regulators responsible for the differential gene expression observed between two conditions (functional relationships) based on gene expression datasets from publicly available data sources. The method is embedded in a sound statistical framework, which integrates infinite mixture modeling with regression analysis. The application of this statistical tool is demonstrated in the study of gene regulation in Escherichia coli data, leading to the detection of biologically relevant associations between environmental conditions and regulators.

 

Keywords: Functional genomics, Finite mixture model, Regression analysis, Gene expression data


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