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Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses

Overview of attention for article published in PLoS Computational Biology, October 2013
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Title
Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003254
Pubmed ID
Authors

Ricardo Aguas, Neil M. Ferguson

Abstract

Despite environmental, social and ecological dependencies, emergence of zoonotic viruses in human populations is clearly also affected by genetic factors which determine cross-species transmission potential. RNA viruses pose an interesting case study given their mutation rates are orders of magnitude higher than any other pathogen--as reflected by the recent emergence of SARS and Influenza for example. Here, we show how feature selection techniques can be used to reliably classify viral sequences by host species, and to identify the crucial minority of host-specific sites in pathogen genomic data. The variability in alleles at those sites can be translated into prediction probabilities that a particular pathogen isolate is adapted to a given host. We illustrate the power of these methods by: 1) identifying the sites explaining SARS coronavirus differences between human, bat and palm civet samples; 2) showing how cross species jumps of rabies virus among bat populations can be readily identified; and 3) de novo identification of likely functional influenza host discriminant markers.

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The data shown below were collected from the profiles of 5 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 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 4%
United States 3 3%
Chile 1 <1%
Japan 1 <1%
Canada 1 <1%
Unknown 100 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 26%
Student > Ph. D. Student 20 18%
Student > Master 11 10%
Professor > Associate Professor 8 7%
Other 8 7%
Other 22 20%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 32%
Biochemistry, Genetics and Molecular Biology 12 11%
Medicine and Dentistry 11 10%
Computer Science 10 9%
Immunology and Microbiology 5 5%
Other 14 13%
Unknown 23 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 01 August 2023.
All research outputs
#14,600,874
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#6,133
of 8,960 outputs
Outputs of similar age
#115,910
of 222,783 outputs
Outputs of similar age from PLoS Computational Biology
#89
of 142 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 29th percentile – i.e., 29% 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 222,783 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 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.