Title |
Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
|
---|---|
Published in |
BMC Bioinformatics, June 2006
|
DOI | 10.1186/1471-2105-7-280 |
Pubmed ID | |
Authors |
David J Reiss, Nitin S Baliga, Richard Bonneau |
Abstract |
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 solutions. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 67% |
Cameroon | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 19 | 6% |
Brazil | 4 | 1% |
United Kingdom | 3 | <1% |
Spain | 3 | <1% |
Hong Kong | 1 | <1% |
Switzerland | 1 | <1% |
Cyprus | 1 | <1% |
Canada | 1 | <1% |
Mexico | 1 | <1% |
Other | 6 | 2% |
Unknown | 276 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 94 | 30% |
Researcher | 82 | 26% |
Student > Master | 36 | 11% |
Professor > Associate Professor | 20 | 6% |
Professor | 16 | 5% |
Other | 45 | 14% |
Unknown | 23 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 134 | 42% |
Computer Science | 53 | 17% |
Biochemistry, Genetics and Molecular Biology | 48 | 15% |
Engineering | 11 | 3% |
Mathematics | 10 | 3% |
Other | 30 | 9% |
Unknown | 30 | 9% |