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Accurate HLA type inference using a weighted similarity graph

Overview of attention for article published in BMC Bioinformatics, December 2010
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Mentioned by

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1 tweeter

Citations

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16 Dimensions

Readers on

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45 Mendeley
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Title
Accurate HLA type inference using a weighted similarity graph
Published in
BMC Bioinformatics, December 2010
DOI 10.1186/1471-2105-11-s11-s10
Pubmed ID
Authors

Minzhu Xie, Jing Li, Tao Jiang

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Mexico 1 2%
South Africa 1 2%
Spain 1 2%
Italy 1 2%
Unknown 39 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 22%
Student > Ph. D. Student 7 16%
Student > Master 6 13%
Student > Bachelor 5 11%
Other 3 7%
Other 8 18%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 29%
Biochemistry, Genetics and Molecular Biology 8 18%
Computer Science 6 13%
Immunology and Microbiology 4 9%
Medicine and Dentistry 4 9%
Other 3 7%
Unknown 7 16%

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 19 September 2016.
All research outputs
#10,995,614
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#4,221
of 4,576 outputs
Outputs of similar age
#219,861
of 263,585 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 29 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% 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 263,585 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.