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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SparSNP: Fast and memory-efficient analysis of all SNPs for phenotype prediction
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-88 |
| Pubmed ID | |
| Authors |
Gad Abraham, Adam Kowalczyk, Justin Zobel, Michael Inouye |
| Abstract |
A central goal of genomics is to predict phenotypic variation from genetic variation. Fitting predictive models to genome-wide and whole genome single nucleotide polymorphism (SNP) profiles allows us to estimate the predictive power of the SNPs and potentially develop diagnostic models for disease. However, many current datasets cannot be analysed with standard tools due to their large size. |
X Demographics
The data shown below were collected from the profiles of 19 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 | 2 | 11% |
| United Kingdom | 2 | 11% |
| Netherlands | 1 | 5% |
| Finland | 1 | 5% |
| Italy | 1 | 5% |
| India | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 10 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 13 | 68% |
| Members of the public | 6 | 32% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Germany | 2 | 2% |
| Switzerland | 1 | 1% |
| Netherlands | 1 | 1% |
| Brazil | 1 | 1% |
| Australia | 1 | 1% |
| Spain | 1 | 1% |
| New Zealand | 1 | 1% |
| Unknown | 76 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 29% |
| Researcher | 25 | 29% |
| Student > Master | 10 | 11% |
| Student > Bachelor | 5 | 6% |
| Student > Doctoral Student | 4 | 5% |
| Other | 12 | 14% |
| Unknown | 6 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 34% |
| Biochemistry, Genetics and Molecular Biology | 16 | 18% |
| Computer Science | 12 | 14% |
| Mathematics | 5 | 6% |
| Medicine and Dentistry | 4 | 5% |
| Other | 9 | 10% |
| Unknown | 11 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 November 2019.
All research outputs
#3,051,756
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#1,039
of 7,400 outputs
Outputs of similar age
#19,977
of 165,046 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 100 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,400 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 done well, scoring higher than 85% 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 165,046 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.