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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Efficient characterization of high-dimensional parameter spaces for systems biology
|
|---|---|
| Published in |
BMC Systems Biology, September 2011
|
| DOI | 10.1186/1752-0509-5-142 |
| Pubmed ID | |
| Authors |
Elías Zamora-Sillero, Marc Hafner, Ariane Ibig, Joerg Stelling, Andreas Wagner |
| Abstract |
A biological system's robustness to mutations and its evolution are influenced by the structure of its viable space, the region of its space of biochemical parameters where it can exert its function. In systems with a large number of biochemical parameters, viable regions with potentially complex geometries fill a tiny fraction of the whole parameter space. This hampers explorations of the viable space based on "brute force" or Gaussian sampling. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 6% |
| Germany | 2 | 1% |
| United Kingdom | 2 | 1% |
| Netherlands | 1 | <1% |
| Sudan | 1 | <1% |
| France | 1 | <1% |
| Portugal | 1 | <1% |
| Switzerland | 1 | <1% |
| Italy | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 160 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 60 | 33% |
| Researcher | 44 | 24% |
| Student > Master | 19 | 10% |
| Student > Bachelor | 9 | 5% |
| Professor > Associate Professor | 7 | 4% |
| Other | 23 | 13% |
| Unknown | 21 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 65 | 36% |
| Biochemistry, Genetics and Molecular Biology | 20 | 11% |
| Computer Science | 19 | 10% |
| Engineering | 12 | 7% |
| Mathematics | 10 | 5% |
| Other | 30 | 16% |
| Unknown | 27 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 November 2017.
All research outputs
#16,047,334
of 25,374,647 outputs
Outputs from BMC Systems Biology
#556
of 1,132 outputs
Outputs of similar age
#92,094
of 137,080 outputs
Outputs of similar age from BMC Systems Biology
#21
of 51 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 46th percentile – i.e., 46% 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 137,080 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 51 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 52% of its contemporaries.