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Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Overview of attention for article published in Genetics Selection Evolution, February 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 410)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter
googleplus
1 Google+ user

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
114 Mendeley
citeulike
2 CiteULike
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Title
Genome-wide prediction of discrete traits using bayesian regressions and machine learning
Published in
Genetics Selection Evolution, February 2011
DOI 10.1186/1297-9686-43-7
Pubmed ID
Authors

Oscar González-Recio, Selma Forni

Abstract

Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates) small n (number of observations) problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance). It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context.

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 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Brazil 2 2%
Peru 1 <1%
Finland 1 <1%
France 1 <1%
Belgium 1 <1%
Spain 1 <1%
Japan 1 <1%
Colombia 1 <1%
Other 1 <1%
Unknown 100 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 27%
Student > Ph. D. Student 27 24%
Student > Master 15 13%
Student > Doctoral Student 6 5%
Professor > Associate Professor 6 5%
Other 23 20%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 63%
Computer Science 12 11%
Mathematics 5 4%
Social Sciences 3 3%
Biochemistry, Genetics and Molecular Biology 3 3%
Other 11 10%
Unknown 8 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 21 April 2014.
All research outputs
#1,336,408
of 12,004,493 outputs
Outputs from Genetics Selection Evolution
#27
of 410 outputs
Outputs of similar age
#17,034
of 134,140 outputs
Outputs of similar age from Genetics Selection Evolution
#2
of 10 outputs
Altmetric has tracked 12,004,493 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 410 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done particularly well, scoring higher than 93% 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 134,140 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 87% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.