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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-102 |
| Pubmed ID | |
| Authors |
Xuan Guo, Yu Meng, Ning Yu, Yi Pan |
| Abstract |
Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Germany | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Denmark | 1 | 2% |
| Korea, Republic of | 1 | 2% |
| United States | 1 | 2% |
| Luxembourg | 1 | 2% |
| Philippines | 1 | 2% |
| Unknown | 45 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 27% |
| Researcher | 12 | 24% |
| Student > Master | 10 | 20% |
| Professor > Associate Professor | 5 | 10% |
| Professor | 2 | 4% |
| Other | 6 | 12% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 39% |
| Agricultural and Biological Sciences | 11 | 22% |
| Biochemistry, Genetics and Molecular Biology | 8 | 16% |
| Mathematics | 3 | 6% |
| Neuroscience | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 4 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 15 April 2014.
All research outputs
#14,779,591
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#5,041
of 7,269 outputs
Outputs of similar age
#128,915
of 228,161 outputs
Outputs of similar age from BMC Bioinformatics
#67
of 118 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 228,161 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.