Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks.

ABSTRACT:

Background

The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively co-regulated groups, as opposed to those that are simply co-expressed. Be cause genes may be co-regulated only across a subset of all observed experimental conditions, biclustering (clustering of genes and conditions) is more appropriate than standard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically, and/or evolutionarily) associated, and such a priori known or pre-computed associations can provide support for appropriately grouping genes. One important association is the presence of one or more common cis-r

SUBMITTER: Reiss DJ 

PROVIDER: S-EPMC1502140 | biostudies-literature | 2006 Jun

REPOSITORIES: biostudies-literature