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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational and statistical approaches to analyzing variants identified by exome sequencing
|
|---|---|
| Published in |
Genome Biology, September 2011
|
| DOI | 10.1186/gb-2011-12-9-227 |
| Pubmed ID | |
| Authors |
Nathan O Stitziel, Adam Kiezun, Shamil Sunyaev |
| Abstract |
New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged. |
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 % |
|---|---|---|
| Mexico | 2 | 50% |
| Greece | 1 | 25% |
| United Kingdom | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 534 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 22 | 4% |
| United Kingdom | 8 | 1% |
| Germany | 6 | 1% |
| France | 5 | <1% |
| Spain | 4 | <1% |
| Belgium | 4 | <1% |
| Italy | 4 | <1% |
| Brazil | 4 | <1% |
| India | 4 | <1% |
| Other | 16 | 3% |
| Unknown | 457 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 168 | 31% |
| Student > Ph. D. Student | 128 | 24% |
| Student > Master | 46 | 9% |
| Student > Bachelor | 34 | 6% |
| Professor > Associate Professor | 31 | 6% |
| Other | 99 | 19% |
| Unknown | 28 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 268 | 50% |
| Biochemistry, Genetics and Molecular Biology | 94 | 18% |
| Medicine and Dentistry | 74 | 14% |
| Computer Science | 22 | 4% |
| Mathematics | 14 | 3% |
| Other | 27 | 5% |
| Unknown | 35 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 16 April 2024.
All research outputs
#1,832,677
of 25,373,627 outputs
Outputs from Genome Biology
#1,522
of 4,467 outputs
Outputs of similar age
#8,561
of 137,122 outputs
Outputs of similar age from Genome Biology
#13
of 46 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 65% 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 137,122 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.