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Mendeley readers
| Title |
A computational framework for gene regulatory network inference that combines multiple methods and datasets
|
|---|---|
| Published in |
BMC Systems Biology, April 2011
|
| DOI | 10.1186/1752-0509-5-52 |
| Pubmed ID | |
| Authors |
Rita Gupta, Anna Stincone, Philipp Antczak, Sarah Durant, Roy Bicknell, Andreas Bikfalvi, Francesco Falciani |
| Abstract |
Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective. |
Mendeley readers
The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| France | 2 | 2% |
| Denmark | 2 | 2% |
| United Kingdom | 2 | 2% |
| Brazil | 1 | <1% |
| Sweden | 1 | <1% |
| Latvia | 1 | <1% |
| India | 1 | <1% |
| Ukraine | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 92 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 28% |
| Student > Ph. D. Student | 26 | 25% |
| Student > Master | 15 | 14% |
| Professor > Associate Professor | 8 | 8% |
| Other | 6 | 6% |
| Other | 15 | 14% |
| Unknown | 6 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 41 | 39% |
| Computer Science | 24 | 23% |
| Biochemistry, Genetics and Molecular Biology | 10 | 9% |
| Environmental Science | 4 | 4% |
| Engineering | 3 | 3% |
| Other | 12 | 11% |
| Unknown | 12 | 11% |