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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Iterative rank-order normalization of gene expression microarray data
|
|---|---|
| Published in |
BMC Bioinformatics, May 2013
|
| DOI | 10.1186/1471-2105-14-153 |
| Pubmed ID | |
| Authors |
Eric A Welsh, Steven A Eschrich, Anders E Berglund, David A Fenstermacher |
| Abstract |
Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 20% |
| United Kingdom | 1 | 10% |
| United States | 1 | 10% |
| Norway | 1 | 10% |
| Germany | 1 | 10% |
| Spain | 1 | 10% |
| Unknown | 3 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 50% |
| Scientists | 5 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 2% |
| United States | 1 | 2% |
| Ukraine | 1 | 2% |
| Belgium | 1 | 2% |
| Unknown | 43 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 28% |
| Researcher | 12 | 26% |
| Student > Master | 5 | 11% |
| Professor > Associate Professor | 3 | 6% |
| Student > Bachelor | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 6 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 26% |
| Biochemistry, Genetics and Molecular Biology | 9 | 19% |
| Computer Science | 5 | 11% |
| Medicine and Dentistry | 4 | 9% |
| Psychology | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 9 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 10 March 2020.
All research outputs
#2,404,887
of 22,709,015 outputs
Outputs from BMC Bioinformatics
#747
of 7,256 outputs
Outputs of similar age
#21,452
of 193,543 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 124 outputs
Altmetric has tracked 22,709,015 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,256 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 done well, scoring higher than 89% 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 193,543 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 88% of its contemporaries.
We're also able to compare this research output to 124 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.