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.
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 February 2021.
All research outputs
#3,941,275
of 26,547,438 outputs
Outputs from Genetics Selection Evolution
#81
of 833 outputs
Outputs of similar age
#17,089
of 121,651 outputs
Outputs of similar age from Genetics Selection Evolution
#1
of 5 outputs
Altmetric has tracked 26,547,438 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 833 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 90% of its peers.
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We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them
