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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Generalization of the normal-exponential model: exploration of a more accurate parametrisation for the signal distribution on Illumina BeadArrays
|
|---|---|
| Published in |
BMC Bioinformatics, December 2012
|
| DOI | 10.1186/1471-2105-13-329 |
| Pubmed ID | |
| Authors |
Sandra Plancade, Yves Rozenholc, Eiliv Lund |
| Abstract |
Illumina BeadArray technology includes non specific negative control features that allow a precise estimation of the background noise. As an alternative to the background subtraction proposed in BeadStudio which leads to an important loss of information by generating negative values, a background correction method modeling the observed intensities as the sum of the exponentially distributed signal and normally distributed noise has been developed. Nevertheless, Wang and Ye (2012) display a kernel-based estimator of the signal distribution on Illumina BeadArrays and suggest that a gamma distribution would represent a better modeling of the signal density. Hence, the normal-exponential modeling may not be appropriate for Illumina data and background corrections derived from this model may lead to wrong estimation. |
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 % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 28% |
| Student > Ph. D. Student | 7 | 24% |
| Student > Master | 5 | 17% |
| Student > Doctoral Student | 2 | 7% |
| Other | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 3 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 21% |
| Mathematics | 5 | 17% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Computer Science | 3 | 10% |
| Business, Management and Accounting | 2 | 7% |
| Other | 7 | 24% |
| Unknown | 3 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 06 December 2023.
All research outputs
#5,038,035
of 24,950,117 outputs
Outputs from BMC Bioinformatics
#1,771
of 7,613 outputs
Outputs of similar age
#49,643
of 290,953 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 137 outputs
Altmetric has tracked 24,950,117 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,613 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 76% 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 290,953 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.