Title |
A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development
|
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Published in |
BMC Systems Biology, June 2012
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DOI | 10.1186/1752-0509-6-77 |
Pubmed ID | |
Authors |
Brandilyn Stigler, Helen M Chamberlin |
Abstract |
Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 3% |
Unknown | 35 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 10 | 28% |
Researcher | 7 | 19% |
Professor > Associate Professor | 4 | 11% |
Student > Master | 4 | 11% |
Student > Bachelor | 3 | 8% |
Other | 5 | 14% |
Unknown | 3 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 12 | 33% |
Biochemistry, Genetics and Molecular Biology | 9 | 25% |
Computer Science | 4 | 11% |
Physics and Astronomy | 2 | 6% |
Chemistry | 2 | 6% |
Other | 3 | 8% |
Unknown | 4 | 11% |