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Mendeley readers
| Title |
Inference of gene regulatory networks from time series by Tsallis entropy
|
|---|---|
| Published in |
BMC Systems Biology, May 2011
|
| DOI | 10.1186/1752-0509-5-61 |
| Pubmed ID | |
| Authors |
Fabrício Martins Lopes, Evaldo A de Oliveira, Roberto M Cesar |
| Abstract |
The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 3 | 4% |
| United Kingdom | 2 | 3% |
| France | 1 | 1% |
| Denmark | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 65 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 18 | 25% |
| Researcher | 15 | 21% |
| Student > Ph. D. Student | 15 | 21% |
| Professor > Associate Professor | 5 | 7% |
| Student > Bachelor | 4 | 5% |
| Other | 11 | 15% |
| Unknown | 5 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 23 | 32% |
| Agricultural and Biological Sciences | 17 | 23% |
| Biochemistry, Genetics and Molecular Biology | 9 | 12% |
| Physics and Astronomy | 8 | 11% |
| Engineering | 5 | 7% |
| Other | 5 | 7% |
| Unknown | 6 | 8% |