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Mendeley readers
Attention Score in Context
| Title |
Multi-population genomic prediction using a multi-task Bayesian learning model
|
|---|---|
| Published in |
BMC Genomic Data, May 2014
|
| DOI | 10.1186/1471-2156-15-53 |
| Pubmed ID | |
| Authors |
Liuhong Chen, Changxi Li, Stephen Miller, Flavio Schenkel |
| Abstract |
Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across populations. The goal of this study was to develop a multi-task Bayesian learning model for multi-population genomic prediction with a strategy to effectively share information across populations. Simulation studies and real data from Holstein and Ayrshire dairy breeds with phenotypes on five milk production traits were used to evaluate the proposed multi-task Bayesian learning model and compare with a single-task model and a simple data pooling method. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 41 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 30% |
| Researcher | 11 | 26% |
| Student > Master | 4 | 9% |
| Professor | 2 | 5% |
| Student > Doctoral Student | 2 | 5% |
| Other | 4 | 9% |
| Unknown | 7 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 22 | 51% |
| Biochemistry, Genetics and Molecular Biology | 3 | 7% |
| Social Sciences | 3 | 7% |
| Chemistry | 3 | 7% |
| Mathematics | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 7 | 16% |
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 19 May 2014.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from BMC Genomic Data
#861
of 1,204 outputs
Outputs of similar age
#178,119
of 241,989 outputs
Outputs of similar age from BMC Genomic Data
#14
of 25 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.