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Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data

Overview of attention for article published in BMC Bioinformatics, September 2021
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
13 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
41 Mendeley
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Title
Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data
Published in
BMC Bioinformatics, September 2021
DOI 10.1186/s12859-021-04370-7
Pubmed ID
Authors

Victoria Bourgeais, Farida Zehraoui, Mohamed Ben Hamdoune, Blaise Hanczar

Twitter Demographics

The data shown below were collected from the profiles of 13 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 22%
Student > Master 5 12%
Student > Doctoral Student 2 5%
Student > Bachelor 2 5%
Professor 2 5%
Other 5 12%
Unknown 16 39%
Readers by discipline Count As %
Computer Science 12 29%
Agricultural and Biological Sciences 3 7%
Biochemistry, Genetics and Molecular Biology 3 7%
Sports and Recreations 1 2%
Social Sciences 1 2%
Other 3 7%
Unknown 18 44%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 13 October 2021.
All research outputs
#2,228,584
of 22,713,403 outputs
Outputs from BMC Bioinformatics
#641
of 7,259 outputs
Outputs of similar age
#52,928
of 429,623 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 149 outputs
Altmetric has tracked 22,713,403 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,259 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 done particularly well, scoring higher than 91% 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 429,623 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 87% of its contemporaries.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.