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 novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies
|
|---|---|
| Published in |
BMC Bioinformatics, October 2012
|
| DOI | 10.1186/1471-2105-13-284 |
| Pubmed ID | |
| Authors |
Verena Zuber, A Pedro Duarte Silva, Korbinian Strimmer |
| Abstract |
Identification of causal SNPs in most genome wide association studies relies on approaches that consider each SNP individually. However, there is a strong correlation structure among SNPs that needs to be taken into account. Hence, increasingly modern computationally expensive regression methods are employed for SNP selection that consider all markers simultaneously and thus incorporate dependencies among SNPs. |
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 % |
|---|---|---|
| Peru | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| United States | 2 | 3% |
| Germany | 1 | 2% |
| Brazil | 1 | 2% |
| Netherlands | 1 | 2% |
| Denmark | 1 | 2% |
| France | 1 | 2% |
| Unknown | 53 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 42% |
| Student > Ph. D. Student | 13 | 21% |
| Student > Master | 7 | 11% |
| Professor > Associate Professor | 5 | 8% |
| Student > Doctoral Student | 4 | 6% |
| Other | 4 | 6% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 31% |
| Computer Science | 11 | 18% |
| Mathematics | 8 | 13% |
| Biochemistry, Genetics and Molecular Biology | 6 | 10% |
| Medicine and Dentistry | 4 | 6% |
| Other | 10 | 16% |
| Unknown | 4 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 05 November 2012.
All research outputs
#6,750,112
of 22,663,969 outputs
Outputs from BMC Bioinformatics
#2,582
of 7,246 outputs
Outputs of similar age
#51,792
of 184,171 outputs
Outputs of similar age from BMC Bioinformatics
#42
of 108 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,246 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 63% 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 184,171 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 71% of its contemporaries.
We're also able to compare this research output to 108 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 61% of its contemporaries.