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Reconciliation and local gene tree rearrangement can be of mutual profit

Overview of attention for article published in Algorithms for Molecular Biology, April 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)

Mentioned by

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1 tweeter
wikipedia
2 Wikipedia pages

Citations

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26 Dimensions

Readers on

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24 Mendeley
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Title
Reconciliation and local gene tree rearrangement can be of mutual profit
Published in
Algorithms for Molecular Biology, April 2013
DOI 10.1186/1748-7188-8-12
Pubmed ID
Authors

Thi Hau Nguyen, Vincent Ranwez, Stéphanie Pointet, Anne-Muriel Arigon Chifolleau, Jean-Philippe Doyon, Vincent Berry

Abstract

Reconciliation methods compare gene trees and species trees to recover evolutionary events such as duplications, transfers and losses explaining the history and composition of genomes. It is well-known that gene trees inferred from molecular sequences can be partly erroneous due to incorrect sequence alignments as well as phylogenetic reconstruction artifacts such as long branch attraction. In practice, this leads reconciliation methods to overestimate the number of evolutionary events. Several methods have been proposed to circumvent this problem, by collapsing the unsupported edges and then resolving the obtained multifurcating nodes, or by directly rearranging the binary gene trees. Yet these methods have been defined for models of evolution accounting only for duplications and losses, i.e. can not be applied to handle prokaryotic gene families.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 46%
Researcher 5 21%
Professor 2 8%
Student > Master 2 8%
Other 1 4%
Other 0 0%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 42%
Computer Science 6 25%
Biochemistry, Genetics and Molecular Biology 3 13%
Medicine and Dentistry 1 4%
Unknown 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 October 2016.
All research outputs
#5,137,023
of 17,086,389 outputs
Outputs from Algorithms for Molecular Biology
#54
of 230 outputs
Outputs of similar age
#46,833
of 161,186 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 4 outputs
Altmetric has tracked 17,086,389 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 230 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 75% 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 161,186 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 69% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them