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Attention Score in Context
| Title |
Optimization of genomic selection training populations with a genetic algorithm
|
|---|---|
| Published in |
Genetics Selection Evolution, May 2015
|
| DOI | 10.1186/s12711-015-0116-6 |
| Pubmed ID | |
| Authors |
Deniz Akdemir, Julio I Sanchez, Jean-Luc Jannink |
| Abstract |
In this article, we imagine a breeding scenario with a population of individuals that have been genotyped but not phenotyped. We derived a computationally efficient statistic that uses this genetic information to measure the reliability of genomic estimated breeding values (GEBV) for a given set of individuals (test set) based on a training set of individuals. We used this reliability measure with a genetic algorithm scheme to find an optimized training set from a larger set of candidate individuals. This subset was phenotyped to create the training set that was used in a genomic selection model to estimate GEBV in the test set. Our results show that, compared to a random sample of the same size, the use of a set of individuals selected by our method improved accuracies. We implemented the proposed training selection methodology on four sets of data on Arabidopsis, wheat, rice and maize. This dynamic model building process that takes genotypes of the individuals in the test sample into account while selecting the training individuals improves the performance of genomic selection models. |
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 % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 230 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | <1% |
| United States | 2 | <1% |
| Italy | 1 | <1% |
| New Zealand | 1 | <1% |
| Sweden | 1 | <1% |
| Denmark | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 221 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 52 | 23% |
| Researcher | 47 | 20% |
| Student > Doctoral Student | 26 | 11% |
| Student > Master | 25 | 11% |
| Student > Bachelor | 10 | 4% |
| Other | 37 | 16% |
| Unknown | 33 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 159 | 69% |
| Biochemistry, Genetics and Molecular Biology | 8 | 3% |
| Mathematics | 5 | 2% |
| Computer Science | 4 | 2% |
| Engineering | 3 | 1% |
| Other | 7 | 3% |
| Unknown | 44 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 22 March 2016.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Genetics Selection Evolution
#667
of 822 outputs
Outputs of similar age
#206,514
of 279,099 outputs
Outputs of similar age from Genetics Selection Evolution
#19
of 23 outputs
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So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.