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Identification of natural selection in genomic data with deep convolutional neural network

Overview of attention for article published in BioData Mining, December 2021
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Mentioned by

twitter
1 tweeter

Readers on

mendeley
5 Mendeley
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Title
Identification of natural selection in genomic data with deep convolutional neural network
Published in
BioData Mining, December 2021
DOI 10.1186/s13040-021-00280-9
Authors

Arnaud Nguembang Fadja, Fabrizio Riguzzi, Giorgio Bertorelle, Emiliano Trucchi

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 %
Lecturer 1 20%
Student > Doctoral Student 1 20%
Student > Master 1 20%
Researcher 1 20%
Professor > Associate Professor 1 20%
Other 0 0%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 40%
Computer Science 1 20%
Agricultural and Biological Sciences 1 20%
Unknown 1 20%

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 06 December 2021.
All research outputs
#17,251,418
of 21,346,872 outputs
Outputs from BioData Mining
#248
of 298 outputs
Outputs of similar age
#333,001
of 461,382 outputs
Outputs of similar age from BioData Mining
#28
of 34 outputs
Altmetric has tracked 21,346,872 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 298 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 8th percentile – i.e., 8% 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 461,382 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.