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Mendeley readers
Attention Score in Context
| Title |
Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0
|
|---|---|
| Published in |
BMC Systems Biology, January 2013
|
| DOI | 10.1186/1752-0509-7-1 |
| Pubmed ID | |
| Authors |
Michael Weber, Sebastian G Henkel, Sebastian Vlaic, Reinhard Guthke, Everardus J van Zoelen, Dominik Driesch |
| Abstract |
Inference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time. |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Turkey | 2 | 3% |
| Germany | 1 | 1% |
| Ireland | 1 | 1% |
| United Kingdom | 1 | 1% |
| Russia | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 62 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 22 | 32% |
| Student > Ph. D. Student | 22 | 32% |
| Student > Master | 9 | 13% |
| Student > Doctoral Student | 4 | 6% |
| Professor | 4 | 6% |
| Other | 5 | 7% |
| Unknown | 3 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 43% |
| Biochemistry, Genetics and Molecular Biology | 10 | 14% |
| Computer Science | 8 | 12% |
| Engineering | 3 | 4% |
| Environmental Science | 3 | 4% |
| Other | 10 | 14% |
| Unknown | 5 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 20 May 2020.
All research outputs
#7,422,018
of 22,691,736 outputs
Outputs from BMC Systems Biology
#314
of 1,142 outputs
Outputs of similar age
#84,159
of 280,698 outputs
Outputs of similar age from BMC Systems Biology
#12
of 54 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 64% of its peers.
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 280,698 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.