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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Copy number variation signature to predict human ancestry
|
|---|---|
| Published in |
BMC Bioinformatics, December 2012
|
| DOI | 10.1186/1471-2105-13-336 |
| Pubmed ID | |
| Authors |
Melissa Pronold, Marzieh Vali, Roger Pique-Regi, Shahab Asgharzadeh |
| Abstract |
Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 25% |
| Norway | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 1 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 13% |
| Sweden | 1 | 3% |
| Belgium | 1 | 3% |
| Unknown | 32 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 28% |
| Student > Ph. D. Student | 10 | 26% |
| Student > Postgraduate | 4 | 10% |
| Student > Doctoral Student | 3 | 8% |
| Professor > Associate Professor | 3 | 8% |
| Other | 7 | 18% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 25 | 64% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Medicine and Dentistry | 2 | 5% |
| Computer Science | 2 | 5% |
| Mathematics | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 1 | 3% |
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 January 2013.
All research outputs
#14,931,785
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#4,825
of 7,454 outputs
Outputs of similar age
#173,442
of 285,938 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 126 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 31st percentile – i.e., 31% 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 285,938 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 126 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.