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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Efficient haplotype block recognition of very long and dense genetic sequences
|
|---|---|
| Published in |
BMC Bioinformatics, January 2014
|
| DOI | 10.1186/1471-2105-15-10 |
| Pubmed ID | |
| Authors |
Daniel Taliun, Johann Gamper, Cristian Pattaro |
| Abstract |
The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n2) time and memory complexity. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 25% |
| Norway | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 1% |
| Colombia | 1 | 1% |
| Germany | 1 | 1% |
| Uruguay | 1 | 1% |
| Brazil | 1 | 1% |
| Sweden | 1 | 1% |
| United Kingdom | 1 | 1% |
| Spain | 1 | 1% |
| United States | 1 | 1% |
| Other | 0 | 0% |
| Unknown | 77 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 30% |
| Student > Ph. D. Student | 22 | 26% |
| Student > Master | 8 | 9% |
| Student > Bachelor | 6 | 7% |
| Student > Doctoral Student | 5 | 6% |
| Other | 9 | 10% |
| Unknown | 10 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 41% |
| Biochemistry, Genetics and Molecular Biology | 21 | 24% |
| Computer Science | 8 | 9% |
| Medicine and Dentistry | 5 | 6% |
| Engineering | 2 | 2% |
| Other | 4 | 5% |
| Unknown | 11 | 13% |
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 17 November 2016.
All research outputs
#5,725,629
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#2,021
of 7,400 outputs
Outputs of similar age
#65,345
of 310,504 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 101 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 75th 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 gotten more attention than average, scoring higher than 72% 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 310,504 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 78% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.