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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Jimena: efficient computing and system state identification for genetic regulatory networks
|
|---|---|
| Published in |
BMC Bioinformatics, October 2013
|
| DOI | 10.1186/1471-2105-14-306 |
| Pubmed ID | |
| Authors |
Stefan Karl, Thomas Dandekar |
| Abstract |
Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 5% |
| Portugal | 1 | 3% |
| Switzerland | 1 | 3% |
| Unknown | 35 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 28% |
| Student > Ph. D. Student | 8 | 21% |
| Student > Master | 6 | 15% |
| Student > Doctoral Student | 4 | 10% |
| Student > Bachelor | 4 | 10% |
| Other | 3 | 8% |
| Unknown | 3 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 38% |
| Computer Science | 8 | 21% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Medicine and Dentistry | 3 | 8% |
| Psychology | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 4 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 October 2013.
All research outputs
#18,351,676
of 22,727,570 outputs
Outputs from BMC Bioinformatics
#6,300
of 7,266 outputs
Outputs of similar age
#156,693
of 210,284 outputs
Outputs of similar age from BMC Bioinformatics
#90
of 106 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 210,284 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.