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LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting

Overview of attention for article published in BMC Bioinformatics, September 2020
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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 (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

blogs
1 blog
twitter
3 X users

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
35 Mendeley
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Title
LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting
Published in
BMC Bioinformatics, September 2020
DOI 10.1186/s12859-020-03721-0
Pubmed ID
Authors

Yuan Zhang, Fei Ye, Dapeng Xiong, Xieping Gao

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 23%
Student > Master 4 11%
Student > Doctoral Student 3 9%
Student > Ph. D. Student 2 6%
Researcher 2 6%
Other 2 6%
Unknown 14 40%
Readers by discipline Count As %
Medicine and Dentistry 6 17%
Biochemistry, Genetics and Molecular Biology 5 14%
Computer Science 5 14%
Nursing and Health Professions 2 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 3 9%
Unknown 13 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 October 2020.
All research outputs
#3,844,280
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#1,446
of 7,387 outputs
Outputs of similar age
#94,360
of 400,347 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 152 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 80% 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 400,347 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 76% of its contemporaries.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.