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NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

Overview of attention for article published in BMC Bioinformatics, June 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 (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
6 Mendeley
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Title
NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations
Published in
BMC Bioinformatics, June 2019
DOI 10.1186/s12859-019-2956-5
Pubmed ID
Authors

Ying-Lian Gao, Zhen Cui, Jin-Xing Liu, Juan Wang, Chun-Hou Zheng

Twitter Demographics

The data shown below were collected from the profiles of 3 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 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 17%
Professor 1 17%
Librarian 1 17%
Student > Ph. D. Student 1 17%
Student > Master 1 17%
Other 0 0%
Unknown 1 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 17%
Computer Science 1 17%
Sports and Recreations 1 17%
Medicine and Dentistry 1 17%
Unknown 2 33%

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 30 June 2019.
All research outputs
#2,371,173
of 15,348,605 outputs
Outputs from BMC Bioinformatics
#1,003
of 5,606 outputs
Outputs of similar age
#57,762
of 262,979 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 28 outputs
Altmetric has tracked 15,348,605 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,606 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 82% 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 262,979 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 77% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.