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Predicting adverse drug reactions through interpretable deep learning framework

Overview of attention for article published in BMC Bioinformatics, December 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
111 Dimensions

Readers on

mendeley
155 Mendeley
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Title
Predicting adverse drug reactions through interpretable deep learning framework
Published in
BMC Bioinformatics, December 2018
DOI 10.1186/s12859-018-2544-0
Pubmed ID
Authors

Sanjoy Dey, Heng Luo, Achille Fokoue, Jianying Hu, Ping Zhang

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 155 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 155 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 15%
Student > Ph. D. Student 20 13%
Researcher 19 12%
Student > Bachelor 15 10%
Student > Doctoral Student 10 6%
Other 15 10%
Unknown 52 34%
Readers by discipline Count As %
Computer Science 30 19%
Biochemistry, Genetics and Molecular Biology 13 8%
Pharmacology, Toxicology and Pharmaceutical Science 9 6%
Chemistry 8 5%
Medicine and Dentistry 8 5%
Other 30 19%
Unknown 57 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 February 2019.
All research outputs
#7,581,674
of 23,120,280 outputs
Outputs from BMC Bioinformatics
#3,050
of 7,330 outputs
Outputs of similar age
#156,659
of 437,310 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 213 outputs
Altmetric has tracked 23,120,280 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,330 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 437,310 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 213 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.