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Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

Overview of attention for article published in Journal of Translational Medicine, April 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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2 X users

Citations

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5 Dimensions

Readers on

mendeley
32 Mendeley
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Title
Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study
Published in
Journal of Translational Medicine, April 2022
DOI 10.1186/s12967-022-03349-z
Pubmed ID
Authors

Mahdi Akbarzadeh, Nadia Alipour, Hamed Moheimani, Asieh Sadat Zahedi, Firoozeh Hosseini-Esfahani, Hossein Lanjanian, Fereidoun Azizi, Maryam S. Daneshpour

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 13%
Student > Master 3 9%
Researcher 2 6%
Professor 1 3%
Unspecified 1 3%
Other 2 6%
Unknown 19 59%
Readers by discipline Count As %
Computer Science 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Business, Management and Accounting 2 6%
Engineering 2 6%
Nursing and Health Professions 2 6%
Other 3 9%
Unknown 18 56%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 April 2022.
All research outputs
#13,865,807
of 23,515,383 outputs
Outputs from Journal of Translational Medicine
#1,658
of 4,170 outputs
Outputs of similar age
#192,205
of 443,749 outputs
Outputs of similar age from Journal of Translational Medicine
#36
of 105 outputs
Altmetric has tracked 23,515,383 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,170 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has gotten more attention than average, scoring higher than 58% 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 443,749 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 54% of its contemporaries.
We're also able to compare this research output to 105 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 62% of its contemporaries.