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Polygenic risk prediction based on singular value decomposition with applications to alcohol use disorder

Overview of attention for article published in BMC Bioinformatics, January 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)

Mentioned by

twitter
5 X users

Readers on

mendeley
11 Mendeley
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Title
Polygenic risk prediction based on singular value decomposition with applications to alcohol use disorder
Published in
BMC Bioinformatics, January 2022
DOI 10.1186/s12859-022-04566-5
Pubmed ID
Authors

James J. Yang, Xi Luo, Elisa M. Trucco, Anne Buu

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 18%
Student > Doctoral Student 2 18%
Researcher 1 9%
Professor > Associate Professor 1 9%
Unknown 5 45%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 27%
Nursing and Health Professions 1 9%
Psychology 1 9%
Engineering 1 9%
Unknown 5 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 15 January 2022.
All research outputs
#14,287,221
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#4,575
of 7,388 outputs
Outputs of similar age
#236,225
of 510,373 outputs
Outputs of similar age from BMC Bioinformatics
#92
of 139 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,388 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 34th percentile – i.e., 34% 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 510,373 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.