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Differential Evolution approach to detect recent admixture

Overview of attention for article published in BMC Genomics, June 2015
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Mentioned by

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3 tweeters

Citations

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17 Dimensions

Readers on

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28 Mendeley
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Title
Differential Evolution approach to detect recent admixture
Published in
BMC Genomics, June 2015
DOI 10.1186/1471-2164-16-s8-s9
Pubmed ID
Authors

Konstantin Kozlov, Dmitri Chebotarev, Mehedi Hassan, Martin Triska, Petr Triska, Pavel Flegontov, Tatiana V Tatarinova

Abstract

The genetic structure of human populations is extraordinarily complex and of fundamental importance to studies of anthropology, evolution, and medicine. As increasingly many individuals are of mixed origin, there is an unmet need for tools that can infer multiple origins. Misclassification of such individuals can lead to incorrect and costly misinterpretations of genomic data, primarily in disease studies and drug trials. We present an advanced tool to infer ancestry that can identify the biogeographic origins of highly mixed individuals. reAdmix can incorporate individual's knowledge of ancestors (e.g. having some ancestors from Turkey or a Scottish grandmother). reAdmix is an online tool available at http://chcb.saban-chla.usc.edu/reAdmix/.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

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 %
Philippines 1 4%
United States 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 25%
Student > Ph. D. Student 6 21%
Professor > Associate Professor 3 11%
Student > Bachelor 3 11%
Student > Master 2 7%
Other 1 4%
Unknown 6 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 25%
Agricultural and Biological Sciences 6 21%
Social Sciences 3 11%
Computer Science 2 7%
Immunology and Microbiology 1 4%
Other 3 11%
Unknown 6 21%

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 26 June 2015.
All research outputs
#7,432,360
of 12,365,836 outputs
Outputs from BMC Genomics
#4,207
of 7,258 outputs
Outputs of similar age
#110,976
of 221,763 outputs
Outputs of similar age from BMC Genomics
#155
of 227 outputs
Altmetric has tracked 12,365,836 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,258 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 36th percentile – i.e., 36% 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 221,763 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 227 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.