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Explaining evolution via constrained persistent perfect phylogeny

Overview of attention for article published in BMC Genomics, October 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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2 X users
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1 Wikipedia page

Citations

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13 Dimensions

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7 Mendeley
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Title
Explaining evolution via constrained persistent perfect phylogeny
Published in
BMC Genomics, October 2014
DOI 10.1186/1471-2164-15-s6-s10
Pubmed ID
Authors

Paola Bonizzoni, Anna Paola Carrieri, Gianluca Della Vedova, Gabriella Trucco

Abstract

The perfect phylogeny is an often used model in phylogenetics since it provides an efficient basic procedure for representing the evolution of genomic binary characters in several frameworks, such as for example in haplotype inference. The model, which is conceptually the simplest, is based on the infinite sites assumption, that is no character can mutate more than once in the whole tree. A main open problem regarding the model is finding generalizations that retain the computational tractability of the original model but are more flexible in modeling biological data when the infinite site assumption is violated because of e.g. back mutations. A special case of back mutations that has been considered in the study of the evolution of protein domains (where a domain is acquired and then lost) is persistency, that is the fact that a character is allowed to return back to the ancestral state. In this model characters can be gained and lost at most once. In this paper we consider the computational problem of explaining binary data by the Persistent Perfect Phylogeny model (referred as PPP) and for this purpose we investigate the problem of reconstructing an evolution where some constraints are imposed on the paths of the tree.

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X Demographics

The data shown below were collected from the profiles of 2 X users 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 14%
Student > Doctoral Student 1 14%
Student > Ph. D. Student 1 14%
Student > Master 1 14%
Student > Postgraduate 1 14%
Other 0 0%
Unknown 2 29%
Readers by discipline Count As %
Computer Science 3 43%
Biochemistry, Genetics and Molecular Biology 2 29%
Nursing and Health Professions 1 14%
Unknown 1 14%
Attention Score in Context

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 10 September 2019.
All research outputs
#6,946,410
of 22,778,347 outputs
Outputs from BMC Genomics
#3,213
of 10,643 outputs
Outputs of similar age
#76,319
of 258,409 outputs
Outputs of similar age from BMC Genomics
#56
of 207 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 10,643 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 68% 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 258,409 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 207 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 71% of its contemporaries.