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HGDTI: predicting drug–target interaction by using information aggregation based on heterogeneous graph neural network

Overview of attention for article published in BMC Bioinformatics, April 2022
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
16 Mendeley
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Title
HGDTI: predicting drug–target interaction by using information aggregation based on heterogeneous graph neural network
Published in
BMC Bioinformatics, April 2022
DOI 10.1186/s12859-022-04655-5
Pubmed ID
Authors

Liyi Yu, Wangren Qiu, Weizhong Lin, Xiang Cheng, Xuan Xiao, Jiexia Dai

Twitter Demographics

The data shown below were collected from the profiles of 7 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 19%
Professor 1 6%
Other 1 6%
Student > Master 1 6%
Unknown 10 63%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 13%
Computer Science 2 13%
Chemistry 2 13%
Engineering 1 6%
Unknown 9 56%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 22 April 2022.
All research outputs
#13,632,656
of 22,287,733 outputs
Outputs from BMC Bioinformatics
#4,407
of 7,148 outputs
Outputs of similar age
#167,123
of 344,348 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 13 outputs
Altmetric has tracked 22,287,733 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,148 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 344,348 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.