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Robustness of birth-death and gain models for inferring evolutionary events

Overview of attention for article published in BMC Genomics, October 2014
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2 tweeters

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Title
Robustness of birth-death and gain models for inferring evolutionary events
Published in
BMC Genomics, October 2014
DOI 10.1186/1471-2164-15-s6-s9
Pubmed ID
Authors

Maureen Stolzer, Larry Wasserman, Dannie Durand

Abstract

Phylogenetic birth-death models are opening a new window on the processes of genome evolution in studies of the evolution of gene and protein families, protein-protein interaction networks, microRNAs, and copy number variation. Given a species tree and a set of genomic characters in present-day species, the birth-death approach estimates the most likely rates required to explain the observed data and returns the expected ancestral character states and the history of character state changes. Achieving a balance between model complexity and generalizability is a fundamental challenge in the application of birth-death models. While more parameters promise greater accuracy and more biologically realistic models, increasing model complexity can lead to overfitting and a heavy computational cost.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 40%
Researcher 8 27%
Student > Master 3 10%
Student > Doctoral Student 2 7%
Unspecified 1 3%
Other 2 7%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 47%
Biochemistry, Genetics and Molecular Biology 6 20%
Computer Science 4 13%
Unspecified 1 3%
Physics and Astronomy 1 3%
Other 1 3%
Unknown 3 10%

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 31 December 2015.
All research outputs
#9,906,325
of 12,373,620 outputs
Outputs from BMC Genomics
#5,668
of 7,313 outputs
Outputs of similar age
#208,189
of 305,960 outputs
Outputs of similar age from BMC Genomics
#234
of 277 outputs
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So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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