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Improvement of phylogenetic method to analyze compositional heterogeneity

Overview of attention for article published in BMC Systems Biology, September 2017
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3 tweeters

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9 Mendeley
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Title
Improvement of phylogenetic method to analyze compositional heterogeneity
Published in
BMC Systems Biology, September 2017
DOI 10.1186/s12918-017-0453-x
Pubmed ID
Authors

Zehua Zhang, Kecheng Guo, Gaofeng Pan, Jijun Tang, Fei Guo

Abstract

Phylogenetic analysis is a key way to understand current research in the biological processes and detect theory in evolution of natural selection. The evolutionary relationship between species is generally reflected in the form of phylogenetic trees. Many methods for constructing phylogenetic trees, are based on the optimization criteria. We extract the biological data via modeling features, and then compare these characteristics to study the biological evolution between species. Here, we use maximum likelihood and Bayesian inference method to establish phylogenetic trees; multi-chain Markov chain Monte Carlo sampling method can be used to select optimal phylogenetic tree, resolving local optimum problem. The correlation model of phylogenetic analysis assumes that phylogenetic trees are built on homogeneous data, however there exists a large deviation in the presence of heterogeneous data. We use conscious detection to solve compositional heterogeneity. Our method is evaluated on two sets of experimental data, a group of bacterial 16S ribosomal RNA gene data, and a group of genetic data with five homologous species. Our method can obtain accurate phylogenetic trees on the homologous data, and also detect the compositional heterogeneity of experimental data. We provide an efficient method to enhance the accuracy of generated phylogenetic tree.

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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 56%
Researcher 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Unknown 1 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 33%
Biochemistry, Genetics and Molecular Biology 2 22%
Social Sciences 2 22%
Engineering 1 11%
Unknown 1 11%

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 19 June 2018.
All research outputs
#10,018,863
of 13,104,802 outputs
Outputs from BMC Systems Biology
#708
of 1,076 outputs
Outputs of similar age
#182,697
of 271,313 outputs
Outputs of similar age from BMC Systems Biology
#9
of 15 outputs
Altmetric has tracked 13,104,802 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,076 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 28th percentile – i.e., 28% 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 271,313 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.