↓ Skip to main content

NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans

Overview of attention for article published in Genome Biology, February 2019
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
40 X users
patent
1 patent

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
103 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans
Published in
Genome Biology, February 2019
DOI 10.1186/s13059-019-1634-2
Pubmed ID
Authors

Barthélémy Caron, Yufei Luo, Antonio Rausell

X Demographics

X Demographics

The data shown below were collected from the profiles of 40 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 103 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 20%
Researcher 19 18%
Student > Master 12 12%
Student > Bachelor 6 6%
Student > Doctoral Student 4 4%
Other 12 12%
Unknown 29 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 42 41%
Medicine and Dentistry 8 8%
Agricultural and Biological Sciences 7 7%
Computer Science 6 6%
Nursing and Health Professions 2 2%
Other 3 3%
Unknown 35 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 27 January 2022.
All research outputs
#1,494,475
of 25,385,509 outputs
Outputs from Genome Biology
#1,194
of 4,468 outputs
Outputs of similar age
#36,787
of 455,881 outputs
Outputs of similar age from Genome Biology
#28
of 62 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 73% 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 455,881 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 62 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 54% of its contemporaries.