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Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

Overview of attention for article published in BMC Bioinformatics, November 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 (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
51 Mendeley
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Title
Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks
Published in
BMC Bioinformatics, November 2020
DOI 10.1186/s12859-020-3437-6
Pubmed ID
Authors

Jin Liu, Guanxin Tan, Wei Lan, Jianxin Wang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 20%
Student > Ph. D. Student 8 16%
Student > Doctoral Student 2 4%
Student > Bachelor 2 4%
Professor 2 4%
Other 5 10%
Unknown 22 43%
Readers by discipline Count As %
Computer Science 15 29%
Medicine and Dentistry 2 4%
Engineering 2 4%
Neuroscience 2 4%
Agricultural and Biological Sciences 1 2%
Other 4 8%
Unknown 25 49%
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 27 November 2020.
All research outputs
#4,281,747
of 23,267,128 outputs
Outputs from BMC Bioinformatics
#1,634
of 7,366 outputs
Outputs of similar age
#115,336
of 506,726 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 168 outputs
Altmetric has tracked 23,267,128 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,366 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 77% 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 506,726 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 76% of its contemporaries.
We're also able to compare this research output to 168 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.