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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Stepwise kinetic equilibrium models of quantitative polymerase chain reaction
|
|---|---|
| Published in |
BMC Bioinformatics, August 2012
|
| DOI | 10.1186/1471-2105-13-203 |
| Pubmed ID | |
| Authors |
Gary Cobbs |
| Abstract |
Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented. |
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 % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 40 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 29% |
| Researcher | 10 | 24% |
| Professor | 7 | 17% |
| Student > Doctoral Student | 2 | 5% |
| Student > Postgraduate | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 39% |
| Biochemistry, Genetics and Molecular Biology | 4 | 10% |
| Medicine and Dentistry | 3 | 7% |
| Mathematics | 2 | 5% |
| Chemical Engineering | 2 | 5% |
| Other | 9 | 22% |
| Unknown | 5 | 12% |
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 02 September 2012.
All research outputs
#15,249,959
of 22,675,759 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,249 outputs
Outputs of similar age
#95,339
of 149,519 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 101 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,249 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 18th percentile – i.e., 18% 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 149,519 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.