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The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Overview of attention for article published in Frontiers in Computational Neuroscience, May 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
1 news outlet
twitter
5 X users
patent
3 patents

Readers on

mendeley
275 Mendeley
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Title
The Application of Unsupervised Clustering Methods to Alzheimer’s Disease
Published in
Frontiers in Computational Neuroscience, May 2019
DOI 10.3389/fncom.2019.00031
Pubmed ID
Authors

Hany Alashwal, Mohamed El Halaby, Jacob J. Crouse, Areeg Abdalla, Ahmed A. Moustafa

Timeline

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X Demographics

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 275 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 14%
Student > Master 38 14%
Researcher 20 7%
Student > Bachelor 15 5%
Student > Doctoral Student 10 4%
Other 39 14%
Unknown 115 42%
Readers by discipline Count As %
Computer Science 40 15%
Engineering 26 9%
Medicine and Dentistry 17 6%
Biochemistry, Genetics and Molecular Biology 14 5%
Neuroscience 12 4%
Other 45 16%
Unknown 121 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 11 March 2023.
All research outputs
#2,185,937
of 26,680,103 outputs
Outputs from Frontiers in Computational Neuroscience
#79
of 1,505 outputs
Outputs of similar age
#44,283
of 367,700 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#2
of 21 outputs
Altmetric has tracked 26,680,103 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,505 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 94% 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 367,700 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.