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BMCBI
2007

EDISA: extracting biclusters from multiple time-series of gene expression profiles

9 years 5 months ago
EDISA: extracting biclusters from multiple time-series of gene expression profiles
Background: Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets. Results: In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression module...
Jochen Supper, Martin Strauch, Dierk Wanke, Klaus
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Jochen Supper, Martin Strauch, Dierk Wanke, Klaus Harter, Andreas Zell
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