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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A new method for modeling coalescent processes with recombination
|
|---|---|
| Published in |
BMC Bioinformatics, August 2014
|
| DOI | 10.1186/1471-2105-15-273 |
| Pubmed ID | |
| Authors |
Ying Wang, Ying Zhou, Linfeng Li, Xian Chen, Yuting Liu, Zhi-Ming Ma, Shuhua Xu |
| Abstract |
Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Hein's, SMC, SMC', and MaCS. |
X Demographics
The data shown below were collected from the profiles of 10 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 | 3 | 30% |
| Denmark | 1 | 10% |
| United Kingdom | 1 | 10% |
| Canada | 1 | 10% |
| Norway | 1 | 10% |
| Unknown | 3 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 4 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 13% |
| Portugal | 1 | 4% |
| Unknown | 20 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 4 | 17% |
| Student > Ph. D. Student | 4 | 17% |
| Researcher | 4 | 17% |
| Professor | 3 | 13% |
| Student > Master | 2 | 8% |
| Other | 3 | 13% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 38% |
| Computer Science | 4 | 17% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Mathematics | 2 | 8% |
| Business, Management and Accounting | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 4 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 26 October 2014.
All research outputs
#6,869,236
of 25,706,302 outputs
Outputs from BMC Bioinformatics
#2,339
of 7,735 outputs
Outputs of similar age
#60,000
of 243,522 outputs
Outputs of similar age from BMC Bioinformatics
#44
of 117 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,735 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 69% 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 243,522 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 74% of its contemporaries.
We're also able to compare this research output to 117 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 60% of its contemporaries.