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X Demographics
Mendeley readers
Attention Score in Context
| Title |
KRLMM: an adaptive genotype calling method for common and low frequency variants
|
|---|---|
| Published in |
BMC Bioinformatics, May 2014
|
| DOI | 10.1186/1471-2105-15-158 |
| Pubmed ID | |
| Authors |
Ruijie Liu, Zhiyin Dai, Meredith Yeager, Rafael A Irizarry, Matthew E Ritchie |
| Abstract |
SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering. |
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 % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Netherlands | 1 | 3% |
| United States | 1 | 3% |
| Australia | 1 | 3% |
| Unknown | 32 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 33% |
| Student > Ph. D. Student | 6 | 17% |
| Student > Master | 6 | 17% |
| Student > Bachelor | 2 | 6% |
| Other | 2 | 6% |
| Other | 6 | 17% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 33% |
| Agricultural and Biological Sciences | 12 | 33% |
| Computer Science | 6 | 17% |
| Medicine and Dentistry | 3 | 8% |
| Chemistry | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 2 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 May 2014.
All research outputs
#18,372,841
of 22,756,196 outputs
Outputs from BMC Bioinformatics
#6,303
of 7,271 outputs
Outputs of similar age
#163,059
of 226,329 outputs
Outputs of similar age from BMC Bioinformatics
#119
of 155 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,271 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 5th percentile – i.e., 5% 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 226,329 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.