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Mendeley readers
Attention Score in Context
| Title |
Estimating parameters for generalized mass action models with connectivity information
|
|---|---|
| Published in |
BMC Bioinformatics, May 2009
|
| DOI | 10.1186/1471-2105-10-140 |
| Pubmed ID | |
| Authors |
Chih-Lung Ko, Eberhard O Voit, Feng-Sheng Wang |
| Abstract |
Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems. |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 10% |
| New Zealand | 1 | 2% |
| Germany | 1 | 2% |
| Vietnam | 1 | 2% |
| Unknown | 41 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 35% |
| Researcher | 11 | 22% |
| Professor > Associate Professor | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Student > Postgraduate | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 29% |
| Engineering | 11 | 22% |
| Biochemistry, Genetics and Molecular Biology | 10 | 20% |
| Computer Science | 2 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 6 | 12% |
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 24 June 2013.
All research outputs
#12,878,328
of 22,714,025 outputs
Outputs from BMC Bioinformatics
#3,783
of 7,259 outputs
Outputs of similar age
#74,841
of 92,528 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 36 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 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 45th percentile – i.e., 45% 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 92,528 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.