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Mendeley readers
Attention Score in Context
| Title |
LD-Spline: Mapping SNPs on genotyping platforms to genomic regions using patterns of linkage disequilibrium
|
|---|---|
| Published in |
BioData Mining, December 2009
|
| DOI | 10.1186/1756-0381-2-7 |
| Pubmed ID | |
| Authors |
William S Bush, Guanhua Chen, Eric S Torstenson, Marylyn D Ritchie |
| Abstract |
Gene-centric analysis tools for genome-wide association study data are being developed both to annotate single locus statistics and to prioritize or group single nucleotide polymorphisms (SNPs) prior to analysis. These approaches require knowledge about the relationships between SNPs on a genotyping platform and genes in the human genome. SNPs in the genome can represent broader genomic regions via linkage disequilibrium (LD), and population-specific patterns of LD can be exploited to generate a data-driven map of SNPs to genes. |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 9% |
| Australia | 2 | 4% |
| Ghana | 1 | 2% |
| Belgium | 1 | 2% |
| Colombia | 1 | 2% |
| Unknown | 36 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 29% |
| Student > Ph. D. Student | 11 | 24% |
| Professor > Associate Professor | 4 | 9% |
| Student > Master | 3 | 7% |
| Student > Bachelor | 2 | 4% |
| Other | 8 | 18% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 53% |
| Biochemistry, Genetics and Molecular Biology | 6 | 13% |
| Medicine and Dentistry | 6 | 13% |
| Computer Science | 2 | 4% |
| Social Sciences | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 5 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 November 2013.
All research outputs
#5,723,945
of 22,731,677 outputs
Outputs from BioData Mining
#118
of 307 outputs
Outputs of similar age
#37,099
of 165,241 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 60% 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 165,241 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 77% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them