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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SeqSIMLA: a sequence and phenotype simulation tool for complex disease studies
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-199 |
| Pubmed ID | |
| Authors |
Ren-Hua Chung, Chung-Chin Shih |
| Abstract |
Association studies based on next-generation sequencing (NGS) technology have become popular, and statistical association tests for NGS data have been developed rapidly. A flexible tool for simulating sequence data in either unrelated case-control or family samples with different disease and quantitative trait models would be useful for evaluating the statistical power for planning a study design and for comparing power among statistical methods based on NGS data. |
X Demographics
The data shown below were collected from the profiles of 8 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 | 3 | 38% |
| Colombia | 1 | 13% |
| Italy | 1 | 13% |
| Norway | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 4 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Sweden | 1 | 3% |
| Norway | 1 | 3% |
| Canada | 1 | 3% |
| Unknown | 28 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 28% |
| Student > Ph. D. Student | 7 | 22% |
| Student > Doctoral Student | 3 | 9% |
| Other | 3 | 9% |
| Professor > Associate Professor | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 38% |
| Biochemistry, Genetics and Molecular Biology | 7 | 22% |
| Computer Science | 3 | 9% |
| Mathematics | 2 | 6% |
| Medicine and Dentistry | 2 | 6% |
| Other | 3 | 9% |
| Unknown | 3 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 08 July 2013.
All research outputs
#6,076,982
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#2,294
of 7,259 outputs
Outputs of similar age
#51,472
of 196,704 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 89 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 68% 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 196,704 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.