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Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

Overview of attention for article published in Frontiers in Pharmacology, August 2020
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
33 Mendeley
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Title
Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine
Published in
Frontiers in Pharmacology, August 2020
DOI 10.3389/fphar.2020.01319
Pubmed ID
Authors

Mohieddin Jafari, Yinyin Wang, Ali Amiryousefi, Jing Tang

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 15%
Student > Master 4 12%
Student > Bachelor 2 6%
Other 2 6%
Researcher 1 3%
Other 0 0%
Unknown 19 58%
Readers by discipline Count As %
Computer Science 5 15%
Biochemistry, Genetics and Molecular Biology 4 12%
Physics and Astronomy 1 3%
Social Sciences 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 20 61%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 06 October 2020.
All research outputs
#7,192,283
of 26,017,215 outputs
Outputs from Frontiers in Pharmacology
#3,152
of 20,002 outputs
Outputs of similar age
#147,109
of 428,125 outputs
Outputs of similar age from Frontiers in Pharmacology
#97
of 453 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 20,002 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 84% 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 428,125 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 65% of its contemporaries.
We're also able to compare this research output to 453 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.