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

ABSTRACT:

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,

SUBMITTER: Supper J 

PROVIDER: S-EPMC2063505 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature