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Stepwise kinetic equilibrium models of quantitative polymerase chain reaction

Overview of attention for article published in BMC Bioinformatics, August 2012
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2 tweeters

Citations

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8 Dimensions

Readers on

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34 Mendeley
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1 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 33 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 32%
Researcher 9 26%
Professor 6 18%
Student > Doctoral Student 2 6%
Other 1 3%
Other 4 12%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 47%
Biochemistry, Genetics and Molecular Biology 3 9%
Medicine and Dentistry 3 9%
Mathematics 2 6%
Chemical Engineering 2 6%
Other 6 18%
Unknown 2 6%

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
#7,762,554
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,576 outputs
Outputs of similar age
#70,485
of 125,511 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 38 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 125,511 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.