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Attention Score in Context
Title |
MethylPCA: a toolkit to control for confounders in methylome-wide association studies
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Published in |
BMC Bioinformatics, March 2013
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DOI | 10.1186/1471-2105-14-74 |
Pubmed ID | |
Authors |
Wenan Chen, Guimin Gao, Srilaxmi Nerella, Christina M Hultman, Patrik KE Magnusson, Patrick F Sullivan, Karolina A Aberg, Edwin JCG van den Oord |
Abstract |
In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 33% |
United States | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Turkey | 1 | 2% |
Italy | 1 | 2% |
Netherlands | 1 | 2% |
Spain | 1 | 2% |
Luxembourg | 1 | 2% |
Unknown | 54 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 34% |
Student > Ph. D. Student | 12 | 20% |
Student > Doctoral Student | 4 | 7% |
Student > Master | 4 | 7% |
Professor | 3 | 5% |
Other | 10 | 16% |
Unknown | 7 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 24 | 39% |
Biochemistry, Genetics and Molecular Biology | 8 | 13% |
Computer Science | 6 | 10% |
Medicine and Dentistry | 4 | 7% |
Neuroscience | 2 | 3% |
Other | 8 | 13% |
Unknown | 9 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 07 March 2013.
All research outputs
#14,746,859
of 22,699,621 outputs
Outputs from BMC Bioinformatics
#5,033
of 7,254 outputs
Outputs of similar age
#117,155
of 193,964 outputs
Outputs of similar age from BMC Bioinformatics
#113
of 160 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 26th percentile – i.e., 26% 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 193,964 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 160 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.