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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MixSIH: a mixture model for single individual haplotyping
|
|---|---|
| Published in |
BMC Genomics, February 2013
|
| DOI | 10.1186/1471-2164-14-s2-s5 |
| Pubmed ID | |
| Authors |
Hirotaka Matsumoto, Hisanori Kiryu |
| Abstract |
Haplotype information is useful for various genetic analyses, including genome-wide association studies. Determining haplotypes experimentally is difficult and there are several computational approaches that infer haplotypes from genomic data. Among such approaches, single individual haplotyping or haplotype assembly, which infers two haplotypes of an individual from aligned sequence fragments, has been attracting considerable attention. To avoid incorrect results in downstream analyses, it is important not only to assemble haplotypes as long as possible but also to provide means to extract highly reliable haplotype regions. Although there are several efficient algorithms for solving haplotype assembly, there are no efficient method that allow for extracting the regions assembled with high confidence. |
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 % |
|---|---|---|
| Australia | 1 | 20% |
| Japan | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 4% |
| New Zealand | 1 | 4% |
| United States | 1 | 4% |
| Unknown | 25 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 29% |
| Researcher | 6 | 21% |
| Professor > Associate Professor | 4 | 14% |
| Student > Bachelor | 3 | 11% |
| Student > Master | 2 | 7% |
| Other | 1 | 4% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 46% |
| Computer Science | 5 | 18% |
| Biochemistry, Genetics and Molecular Biology | 4 | 14% |
| Mathematics | 1 | 4% |
| Engineering | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 4 | 14% |
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 29 September 2021.
All research outputs
#5,389,492
of 22,691,736 outputs
Outputs from BMC Genomics
#2,135
of 10,616 outputs
Outputs of similar age
#63,227
of 307,669 outputs
Outputs of similar age from BMC Genomics
#75
of 357 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,616 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 79% 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 307,669 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 79% of its contemporaries.
We're also able to compare this research output to 357 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.