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Inferring Pareto-optimal reconciliations across multiple event costs under the duplication-loss-coalescence model

Overview of attention for article published in BMC Bioinformatics, December 2019
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Mentioned by

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1 X user

Citations

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7 Dimensions

Readers on

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2 Mendeley
Title
Inferring Pareto-optimal reconciliations across multiple event costs under the duplication-loss-coalescence model
Published in
BMC Bioinformatics, December 2019
DOI 10.1186/s12859-019-3206-6
Pubmed ID
Authors

Ross Mawhorter, Nuo Liu, Ran Libeskind-Hadas, Yi-Chieh Wu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 50%
Student > Ph. D. Student 1 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 50%
Agricultural and Biological Sciences 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 18 December 2019.
All research outputs
#20,595,624
of 23,182,015 outputs
Outputs from BMC Bioinformatics
#6,920
of 7,345 outputs
Outputs of similar age
#359,756
of 430,755 outputs
Outputs of similar age from BMC Bioinformatics
#200
of 227 outputs
Altmetric has tracked 23,182,015 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,345 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 1st percentile – i.e., 1% 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 430,755 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% 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 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.