You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Unknown | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| Brazil | 2 | 1% |
| Colombia | 1 | <1% |
| Finland | 1 | <1% |
| Peru | 1 | <1% |
| France | 1 | <1% |
| Spain | 1 | <1% |
| Belgium | 1 | <1% |
| Japan | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 151 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 36 | 22% |
| Student > Ph. D. Student | 33 | 20% |
| Student > Master | 24 | 15% |
| Professor | 9 | 5% |
| Student > Doctoral Student | 7 | 4% |
| Other | 26 | 16% |
| Unknown | 30 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 80 | 48% |
| Computer Science | 16 | 10% |
| Biochemistry, Genetics and Molecular Biology | 7 | 4% |
| Mathematics | 6 | 4% |
| Medicine and Dentistry | 4 | 2% |
| Other | 13 | 8% |
| Unknown | 39 | 24% |
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 February 2021.
All research outputs
#3,709,773
of 25,373,627 outputs
Outputs from Genetics Selection Evolution
#79
of 822 outputs
Outputs of similar age
#16,403
of 118,451 outputs
Outputs of similar age from Genetics Selection Evolution
#1
of 5 outputs
Altmetric has tracked 25,373,627 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 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 90% 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 118,451 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 86% of its contemporaries.
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