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A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

Overview of attention for article published in Human Genomics, August 2021
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1 X user

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34 Mendeley
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Title
A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants
Published in
Human Genomics, August 2021
DOI 10.1186/s40246-021-00352-1
Pubmed ID
Authors

Maria-Theodora Pandi, Maria Koromina, Iordanis Tsafaridis, Sotirios Patsilinakos, Evangelos Christoforou, Peter J. van der Spek, George P. Patrinos

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 7 21%
Researcher 3 9%
Other 3 9%
Student > Master 3 9%
Professor 2 6%
Other 6 18%
Unknown 10 29%
Readers by discipline Count As %
Unspecified 7 21%
Pharmacology, Toxicology and Pharmaceutical Science 5 15%
Biochemistry, Genetics and Molecular Biology 4 12%
Medicine and Dentistry 3 9%
Agricultural and Biological Sciences 2 6%
Other 4 12%
Unknown 9 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 September 2021.
All research outputs
#20,669,432
of 25,392,582 outputs
Outputs from Human Genomics
#463
of 564 outputs
Outputs of similar age
#330,714
of 437,130 outputs
Outputs of similar age from Human Genomics
#14
of 21 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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 437,130 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
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 is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.