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Identification of recurrent regions of copy-number variants across multiple individuals

Overview of attention for article published in BMC Bioinformatics, March 2010
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

patent
78 patents

Readers on

mendeley
46 Mendeley
citeulike
4 CiteULike
connotea
2 Connotea
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Title
Identification of recurrent regions of copy-number variants across multiple individuals
Published in
BMC Bioinformatics, March 2010
DOI 10.1186/1471-2105-11-147
Pubmed ID
Authors

Teo Shu Mei, Agus Salim, Stefano Calza, Ku Chee Seng, Chia Kee Seng, Yudi Pawitan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 7%
Italy 1 2%
Switzerland 1 2%
Israel 1 2%
Brazil 1 2%
Unknown 39 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 35%
Student > Ph. D. Student 10 22%
Student > Master 5 11%
Professor > Associate Professor 4 9%
Student > Bachelor 2 4%
Other 6 13%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 50%
Biochemistry, Genetics and Molecular Biology 10 22%
Mathematics 3 7%
Computer Science 3 7%
Medicine and Dentistry 2 4%
Other 1 2%
Unknown 4 9%
Attention Score in Context

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 16 April 2024.
All research outputs
#7,752,409
of 23,572,442 outputs
Outputs from BMC Bioinformatics
#3,084
of 7,418 outputs
Outputs of similar age
#35,174
of 95,837 outputs
Outputs of similar age from BMC Bioinformatics
#25
of 61 outputs
Altmetric has tracked 23,572,442 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 95,837 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.