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Mendeley readers
Attention Score in Context
| Title |
Heterogeneous computing architecture for fast detection of SNP-SNP interactions
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-216 |
| Pubmed ID | |
| Authors |
Davor Sluga, Tomaz Curk, Blaz Zupan, Uros Lotric |
| Abstract |
The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 20% |
| Norway | 1 | 20% |
| Japan | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Italy | 1 | 3% |
| Unknown | 28 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 30% |
| Student > Ph. D. Student | 4 | 13% |
| Professor > Associate Professor | 4 | 13% |
| Professor | 3 | 10% |
| Student > Postgraduate | 2 | 7% |
| Other | 4 | 13% |
| Unknown | 4 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 37% |
| Agricultural and Biological Sciences | 6 | 20% |
| Nursing and Health Professions | 2 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Engineering | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 8 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 21 January 2015.
All research outputs
#12,706,817
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#3,621
of 7,272 outputs
Outputs of similar age
#103,643
of 227,908 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 151 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 48th percentile – i.e., 48% 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 227,908 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 54% of its contemporaries.
We're also able to compare this research output to 151 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 56% of its contemporaries.