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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping
|
|---|---|
| Published in |
BMC Genomic Data, February 2013
|
| DOI | 10.1186/1471-2156-14-5 |
| Pubmed ID | |
| Authors |
Anhui Huang, Shizhong Xu, Xiaodong Cai |
| Abstract |
Complex binary traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. Although a number of QTL mapping methods for binary traits have been developed, there still lacks an efficient and powerful method that can handle both main and epistatic effects of a relatively large number of possible QTLs. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Brazil | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 6% |
| Japan | 1 | 3% |
| Spain | 1 | 3% |
| Turkey | 1 | 3% |
| Unknown | 31 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 31% |
| Researcher | 6 | 17% |
| Student > Doctoral Student | 4 | 11% |
| Student > Bachelor | 4 | 11% |
| Professor > Associate Professor | 4 | 11% |
| Other | 3 | 8% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 25% |
| Mathematics | 7 | 19% |
| Computer Science | 4 | 11% |
| Biochemistry, Genetics and Molecular Biology | 3 | 8% |
| Medicine and Dentistry | 2 | 6% |
| Other | 7 | 19% |
| Unknown | 4 | 11% |
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 15 February 2013.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from BMC Genomic Data
#668
of 1,204 outputs
Outputs of similar age
#205,204
of 309,594 outputs
Outputs of similar age from BMC Genomic Data
#11
of 16 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% 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 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 309,594 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.