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 |
A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies
|
|---|---|
| Published in |
PLoS Computational Biology, May 2010
|
| DOI | 10.1371/journal.pcbi.1000770 |
| Pubmed ID | |
| Authors |
Oliver Stegle, Leopold Parts, Richard Durbin, John Winn |
X Demographics
The data shown below were collected from the profiles of 18 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 States | 5 | 28% |
| Germany | 4 | 22% |
| United Kingdom | 1 | 6% |
| Guinea | 1 | 6% |
| Tunisia | 1 | 6% |
| Unknown | 6 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 56% |
| Members of the public | 7 | 39% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 553 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 23 | 4% |
| Germany | 7 | 1% |
| United Kingdom | 3 | <1% |
| France | 2 | <1% |
| Brazil | 2 | <1% |
| Norway | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Netherlands | 1 | <1% |
| New Zealand | 1 | <1% |
| Other | 5 | <1% |
| Unknown | 507 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 177 | 32% |
| Researcher | 134 | 24% |
| Student > Master | 42 | 8% |
| Student > Bachelor | 36 | 7% |
| Professor > Associate Professor | 31 | 6% |
| Other | 81 | 15% |
| Unknown | 52 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 228 | 41% |
| Biochemistry, Genetics and Molecular Biology | 106 | 19% |
| Computer Science | 51 | 9% |
| Mathematics | 34 | 6% |
| Medicine and Dentistry | 18 | 3% |
| Other | 49 | 9% |
| Unknown | 67 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 03 October 2023.
All research outputs
#1,843,971
of 26,017,215 outputs
Outputs from PLoS Computational Biology
#1,593
of 9,038 outputs
Outputs of similar age
#6,308
of 109,092 outputs
Outputs of similar age from PLoS Computational Biology
#6
of 52 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,038 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 82% 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 109,092 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.