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AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides

Overview of attention for article published in Frontiers in Genetics, July 2023
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Mentioned by

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1 X user

Citations

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7 Dimensions

Readers on

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10 Mendeley
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Title
AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides
Published in
Frontiers in Genetics, July 2023
DOI 10.3389/fgene.2023.1232117
Pubmed ID
Authors

Yuanda Wang, Liyang Wang, Chengquan Li, Yilin Pei, Xiaoxiao Liu, Yu Tian

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Lecturer 1 10%
Student > Bachelor 1 10%
Unknown 6 60%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 10%
Linguistics 1 10%
Computer Science 1 10%
Engineering 1 10%
Unknown 6 60%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 July 2023.
All research outputs
#21,509,553
of 26,388,114 outputs
Outputs from Frontiers in Genetics
#8,219
of 13,911 outputs
Outputs of similar age
#272,838
of 371,096 outputs
Outputs of similar age from Frontiers in Genetics
#174
of 357 outputs
Altmetric has tracked 26,388,114 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,911 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 28th percentile – i.e., 28% 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 371,096 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 357 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.