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Editorial: Deciphering Non-Coding Regulatory Variants: Computational and Functional Validation

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, November 2021
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  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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4 X users

Citations

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Readers on

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Title
Editorial: Deciphering Non-Coding Regulatory Variants: Computational and Functional Validation
Published in
Frontiers in Bioengineering and Biotechnology, November 2021
DOI 10.3389/fbioe.2021.769614
Pubmed ID
Authors

Li Chen, Mulin Jun Li

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 100%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 100%
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 03 November 2021.
All research outputs
#15,686,478
of 23,310,485 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#2,724
of 6,955 outputs
Outputs of similar age
#250,566
of 440,526 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#165
of 475 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,955 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 56% 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 440,526 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 475 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 57% of its contemporaries.