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Mendeley readers
Attention Score in Context
| Title |
ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-61 |
| Pubmed ID | |
| Authors |
Mohsen Hajiloo, Yadav Sapkota, John R Mackey, Paula Robson, Russell Greiner, Sambasivarao Damaraju |
| Abstract |
Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case-control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification. |
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 % |
|---|---|---|
| United States | 2 | 50% |
| Canada | 1 | 25% |
| Norway | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| India | 1 | 1% |
| France | 1 | 1% |
| South Africa | 1 | 1% |
| Unknown | 66 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 30% |
| Researcher | 14 | 20% |
| Student > Master | 9 | 13% |
| Student > Bachelor | 5 | 7% |
| Student > Postgraduate | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 25% |
| Medicine and Dentistry | 11 | 15% |
| Computer Science | 10 | 14% |
| Biochemistry, Genetics and Molecular Biology | 5 | 7% |
| Social Sciences | 5 | 7% |
| Other | 10 | 14% |
| Unknown | 12 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 24 February 2013.
All research outputs
#12,810,401
of 22,699,621 outputs
Outputs from BMC Bioinformatics
#3,756
of 7,254 outputs
Outputs of similar age
#98,861
of 192,954 outputs
Outputs of similar age from BMC Bioinformatics
#77
of 140 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% 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 47th percentile – i.e., 47% 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 192,954 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.