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Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences

Overview of attention for article published in Frontiers in Microbiology, February 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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11 X users

Citations

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57 Dimensions

Readers on

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134 Mendeley
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Title
Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences
Published in
Frontiers in Microbiology, February 2020
DOI 10.3389/fmicb.2020.00048
Pubmed ID
Authors

Zhichang Liu, Dun Deng, Huijie Lu, Jian Sun, Luchao Lv, Shuhong Li, Guanghui Peng, Xianyong Ma, Jiazhou Li, Zhenming Li, Ting Rong, Gang Wang

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 134 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 16%
Researcher 16 12%
Student > Bachelor 14 10%
Student > Ph. D. Student 11 8%
Student > Postgraduate 7 5%
Other 14 10%
Unknown 50 37%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 15%
Agricultural and Biological Sciences 17 13%
Computer Science 11 8%
Medicine and Dentistry 6 4%
Immunology and Microbiology 5 4%
Other 18 13%
Unknown 57 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 08 July 2023.
All research outputs
#5,375,216
of 26,017,215 outputs
Outputs from Frontiers in Microbiology
#5,189
of 29,761 outputs
Outputs of similar age
#119,864
of 478,620 outputs
Outputs of similar age from Frontiers in Microbiology
#120
of 661 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 29,761 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done well, scoring higher than 82% 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 478,620 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 74% of its contemporaries.
We're also able to compare this research output to 661 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.