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Evolutionary Pattern and Large-Scale Architecture of Mutation Networks of 2009 A (H1N1) Influenza A Virus

Overview of attention for article published in Frontiers in Genetics, June 2018
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Title
Evolutionary Pattern and Large-Scale Architecture of Mutation Networks of 2009 A (H1N1) Influenza A Virus
Published in
Frontiers in Genetics, June 2018
DOI 10.3389/fgene.2018.00204
Pubmed ID
Authors

Chengmin Wang, Nan Lyu, Lingling Deng, Jing Wang, Wenwen Gu, Hua Ding, Yan Wu, Jing Luo, Liang Wang, Xueze Lyv, Xiaodong Liu, Yi Tao, Hongxuan He

Abstract

The adaptive evolution of influenza virus is an important question, but predicting its evolutionary future will be more challenging. Here, we investigated the mutation characteristic of influenza virus based on the complete genome data of 2009 (H1N1) influenza A virus. By assuming that evolution proceeds via the accumulation of mutations, we analyzed the mutation networks at four different time stages and found that the network structure follows the characteristics of a scale-free network. These results will be important for epidemiology and the future control of influenza viruses. Furthermore, we predicted the predominant mutation virus strain by using the early mutation network of influenza viruses, and this result was consistent with the WHO recommendation for the candidate vaccine of influenza virus. The key contribution of this study is that we explained the biological significance of this scale-free network for influenza pandemic and provided a potential method for predicting the candidate vaccine by using the early-stage network.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 27%
Student > Master 2 18%
Student > Bachelor 2 18%
Other 1 9%
Lecturer 1 9%
Other 1 9%
Unknown 1 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 27%
Biochemistry, Genetics and Molecular Biology 2 18%
Immunology and Microbiology 2 18%
Engineering 2 18%
Physics and Astronomy 1 9%
Other 0 0%
Unknown 1 9%
Attention Score in Context

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 29 June 2018.
All research outputs
#17,978,863
of 23,088,369 outputs
Outputs from Frontiers in Genetics
#6,181
of 12,135 outputs
Outputs of similar age
#238,157
of 329,367 outputs
Outputs of similar age from Frontiers in Genetics
#97
of 129 outputs
Altmetric has tracked 23,088,369 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,135 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 40th percentile – i.e., 40% 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 329,367 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.