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Mendeley readers
Attention Score in Context
| Title |
Dynamic simulation of regulatory networks using SQUAD
|
|---|---|
| Published in |
BMC Bioinformatics, November 2007
|
| DOI | 10.1186/1471-2105-8-462 |
| Pubmed ID | |
| Authors |
Alessandro Di Cara, Abhishek Garg, Giovanni De Micheli, Ioannis Xenarios, Luis Mendoza |
| Abstract |
The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. |
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 % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 3% |
| Netherlands | 2 | 1% |
| Luxembourg | 2 | 1% |
| Portugal | 1 | <1% |
| France | 1 | <1% |
| Switzerland | 1 | <1% |
| Spain | 1 | <1% |
| Cuba | 1 | <1% |
| Unknown | 136 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 41 | 28% |
| Researcher | 40 | 27% |
| Student > Master | 18 | 12% |
| Professor > Associate Professor | 11 | 7% |
| Student > Bachelor | 11 | 7% |
| Other | 19 | 13% |
| Unknown | 9 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 74 | 50% |
| Biochemistry, Genetics and Molecular Biology | 21 | 14% |
| Computer Science | 13 | 9% |
| Engineering | 7 | 5% |
| Medicine and Dentistry | 6 | 4% |
| Other | 16 | 11% |
| Unknown | 12 | 8% |
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 17 September 2014.
All research outputs
#18,378,085
of 22,763,032 outputs
Outputs from BMC Bioinformatics
#6,307
of 7,273 outputs
Outputs of similar age
#146,811
of 156,142 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 44 outputs
Altmetric has tracked 22,763,032 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 7,273 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 156,142 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.