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iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks

Overview of attention for article published in BMC Bioinformatics, December 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
7 tweeters

Readers on

mendeley
5 Mendeley
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Title
iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks
Published in
BMC Bioinformatics, December 2019
DOI 10.1186/s12859-019-3295-2
Pubmed ID
Authors

Binh P. Nguyen, Quang H. Nguyen, Giang-Nam Doan-Ngoc, Thanh-Hoang Nguyen-Vo, Susanto Rahardja

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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 1 20%
Lecturer 1 20%
Student > Doctoral Student 1 20%
Lecturer > Senior Lecturer 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 2 40%
Agricultural and Biological Sciences 1 20%
Unknown 2 40%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 17 February 2020.
All research outputs
#4,662,167
of 15,094,596 outputs
Outputs from BMC Bioinformatics
#2,119
of 5,555 outputs
Outputs of similar age
#127,477
of 331,750 outputs
Outputs of similar age from BMC Bioinformatics
#242
of 573 outputs
Altmetric has tracked 15,094,596 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 5,555 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 61% 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 331,750 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 61% of its contemporaries.
We're also able to compare this research output to 573 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.