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Extension of the bayesian alphabet for genomic selection

Overview of attention for article published in BMC Bioinformatics, May 2011
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Title
Extension of the bayesian alphabet for genomic selection
Published in
BMC Bioinformatics, May 2011
DOI 10.1186/1471-2105-12-186
Pubmed ID
Authors

David Habier, Rohan L Fernando, Kadir Kizilkaya, Dorian J Garrick

Abstract

Two bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.

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X Demographics

The data shown below were collected from the profiles of 3 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 632 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 2%
Brazil 8 1%
Germany 3 <1%
Denmark 2 <1%
Sweden 2 <1%
Mexico 2 <1%
Netherlands 1 <1%
United Kingdom 1 <1%
France 1 <1%
Other 5 <1%
Unknown 597 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 161 25%
Researcher 116 18%
Student > Master 98 16%
Student > Doctoral Student 52 8%
Student > Postgraduate 22 3%
Other 77 12%
Unknown 106 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 388 61%
Biochemistry, Genetics and Molecular Biology 52 8%
Veterinary Science and Veterinary Medicine 15 2%
Engineering 13 2%
Mathematics 9 1%
Other 28 4%
Unknown 127 20%
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 30 April 2021.
All research outputs
#15,860,452
of 25,101,232 outputs
Outputs from BMC Bioinformatics
#4,968
of 7,651 outputs
Outputs of similar age
#86,469
of 117,425 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 96 outputs
Altmetric has tracked 25,101,232 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,651 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 31st percentile – i.e., 31% 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 117,425 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.