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Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features

Overview of attention for article published in BMC Bioinformatics, July 2021
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features
Published in
BMC Bioinformatics, July 2021
DOI 10.1186/s12859-021-04305-2
Pubmed ID
Authors

Onkar Singh, Wen-Lian Hsu, Emily Chia-Yu Su

Twitter Demographics

The data shown below were collected from the profiles of 6 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 9 35%
Student > Master 3 12%
Researcher 3 12%
Student > Ph. D. Student 2 8%
Professor 1 4%
Other 1 4%
Unknown 7 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 42%
Unspecified 2 8%
Computer Science 2 8%
Engineering 2 8%
Medicine and Dentistry 1 4%
Other 0 0%
Unknown 8 31%

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 01 August 2021.
All research outputs
#13,531,963
of 21,699,130 outputs
Outputs from BMC Bioinformatics
#4,536
of 7,012 outputs
Outputs of similar age
#176,958
of 340,958 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 10 outputs
Altmetric has tracked 21,699,130 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,012 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 31st percentile – i.e., 31% 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 340,958 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.