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Attention Score in Context
Title |
An exome sequencing pipeline for identifying and genotyping common CNVs associated with disease with application to psoriasis
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Published in |
Bioinformatics, September 2012
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DOI | 10.1093/bioinformatics/bts379 |
Pubmed ID | |
Authors |
Lachlan J.M. Coin, Dandan Cao, Jingjing Ren, Xianbo Zuo, Liangdan Sun, Sen Yang, Xuejun Zhang, Yong Cui, Yingrui Li, Xin Jin, Jun Wang |
Abstract |
Despite the prevalence of copy number variation (CNV) in the human genome, only a handful of confirmed associations have been reported between common CNVs and complex disease. This may be partially attributed to the difficulty in accurately genotyping CNVs in large cohorts using array-based technologies. Exome sequencing is now widely being applied to case-control cohorts and presents an exciting opportunity to look for common CNVs associated with disease. |
X Demographics
The data shown below were collected from the profiles of 10 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 Kingdom | 2 | 20% |
China | 1 | 10% |
Switzerland | 1 | 10% |
Spain | 1 | 10% |
Unknown | 5 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 60% |
Scientists | 3 | 30% |
Unknown | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 3% |
Switzerland | 2 | 2% |
Spain | 1 | 1% |
United Kingdom | 1 | 1% |
Unknown | 79 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 24% |
Student > Ph. D. Student | 18 | 21% |
Student > Master | 10 | 12% |
Student > Postgraduate | 6 | 7% |
Professor > Associate Professor | 6 | 7% |
Other | 16 | 19% |
Unknown | 9 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 35 | 41% |
Computer Science | 15 | 17% |
Biochemistry, Genetics and Molecular Biology | 12 | 14% |
Medicine and Dentistry | 7 | 8% |
Immunology and Microbiology | 2 | 2% |
Other | 4 | 5% |
Unknown | 11 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 14 May 2019.
All research outputs
#2,782,180
of 25,373,627 outputs
Outputs from Bioinformatics
#2,179
of 12,808 outputs
Outputs of similar age
#18,992
of 187,206 outputs
Outputs of similar age from Bioinformatics
#35
of 182 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done well, scoring higher than 82% 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 187,206 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 182 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.