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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Overview of attention for article published in Genome Biology (Online Edition), November 2019
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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 (75th percentile)

Mentioned by

twitter
16 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
52 Mendeley
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Title
RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants
Published in
Genome Biology (Online Edition), November 2019
DOI 10.1186/s13059-019-1847-4
Authors

Hai Lin, Katherine A. Hargreaves, Rudong Li, Jill L. Reiter, Yue Wang, Matthew Mort, David N. Cooper, Yaoqi Zhou, Chi Zhang, Michael T. Eadon, M. Eileen Dolan, Joseph Ipe, Todd C. Skaar, Yunlong Liu

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Researcher 6 12%
Student > Bachelor 6 12%
Student > Master 4 8%
Other 3 6%
Other 8 15%
Unknown 13 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 29%
Agricultural and Biological Sciences 11 21%
Medicine and Dentistry 2 4%
Chemical Engineering 1 2%
Nursing and Health Professions 1 2%
Other 6 12%
Unknown 16 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 31 December 2019.
All research outputs
#2,895,677
of 16,522,185 outputs
Outputs from Genome Biology (Online Edition)
#2,046
of 3,472 outputs
Outputs of similar age
#95,204
of 393,217 outputs
Outputs of similar age from Genome Biology (Online Edition)
#246
of 290 outputs
Altmetric has tracked 16,522,185 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,472 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.3. This one is in the 40th percentile – i.e., 40% 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 393,217 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 75% of its contemporaries.
We're also able to compare this research output to 290 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.