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

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-regulatory motifs. In organisms where these motifs are not known, their de novo detection, integrated into the clustering algorithm, can help to guide the process towards more biologically parsimonious solut...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: David J. Reiss, Nitin S. Baliga, Richard Bonneau
• Keywords: BMCBI 2006, Expression Data, Novo Detection, Regulatory Network