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Mendeley readers
Attention Score in Context
| Title |
RuleMonkey: software for stochastic simulation of rule-based models
|
|---|---|
| Published in |
BMC Bioinformatics, July 2010
|
| DOI | 10.1186/1471-2105-11-404 |
| Pubmed ID | |
| Authors |
Joshua Colvin, Michael I Monine, Ryan N Gutenkunst, William S Hlavacek, Daniel D Von Hoff, Richard G Posner |
| Abstract |
The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| United Kingdom | 2 | 3% |
| Germany | 1 | 2% |
| Unknown | 54 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 27% |
| Researcher | 14 | 23% |
| Professor > Associate Professor | 6 | 10% |
| Student > Bachelor | 5 | 8% |
| Student > Master | 4 | 7% |
| Other | 9 | 15% |
| Unknown | 6 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 32% |
| Computer Science | 14 | 23% |
| Biochemistry, Genetics and Molecular Biology | 9 | 15% |
| Mathematics | 2 | 3% |
| Engineering | 2 | 3% |
| Other | 8 | 13% |
| Unknown | 6 | 10% |
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 28 September 2014.
All research outputs
#7,454,298
of 22,789,076 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#33,397
of 93,983 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 55 outputs
Altmetric has tracked 22,789,076 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 7,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 93,983 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.