↓ Skip to main content

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Overview of attention for article published in BioData Mining, March 2019
Altmetric Badge

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)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
5 X users
patent
2 patents

Citations

dimensions_citation
72 Dimensions

Readers on

mendeley
132 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens
Published in
BioData Mining, March 2019
DOI 10.1186/s13040-019-0196-x
Pubmed ID
Authors

Sebastian Spänig, Dominik Heider

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 132 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 16%
Student > Ph. D. Student 19 14%
Researcher 17 13%
Student > Bachelor 12 9%
Other 7 5%
Other 25 19%
Unknown 31 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 25%
Computer Science 14 11%
Agricultural and Biological Sciences 13 10%
Chemistry 8 6%
Engineering 7 5%
Other 19 14%
Unknown 38 29%
Attention Score in Context

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 05 December 2023.
All research outputs
#4,014,263
of 25,002,811 outputs
Outputs from BioData Mining
#75
of 320 outputs
Outputs of similar age
#79,895
of 359,826 outputs
Outputs of similar age from BioData Mining
#2
of 6 outputs
Altmetric has tracked 25,002,811 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 320 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has done well, scoring higher than 76% 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 359,826 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 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.