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Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, April 2020
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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3 X users

Citations

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21 Dimensions

Readers on

mendeley
8 Mendeley
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Title
Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications
Published in
Frontiers in Bioengineering and Biotechnology, April 2020
DOI 10.3389/fbioe.2020.00274
Pubmed ID
Authors

Rao Zeng, Minghong Liao

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 25%
Unspecified 1 13%
Lecturer 1 13%
Student > Postgraduate 1 13%
Student > Master 1 13%
Other 0 0%
Unknown 2 25%
Readers by discipline Count As %
Computer Science 3 38%
Unspecified 1 13%
Agricultural and Biological Sciences 1 13%
Psychology 1 13%
Unknown 2 25%
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 21 April 2020.
All research outputs
#18,057,409
of 23,202,641 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#2,964
of 6,883 outputs
Outputs of similar age
#266,783
of 374,994 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#233
of 380 outputs
Altmetric has tracked 23,202,641 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,883 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 49th percentile – i.e., 49% 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 374,994 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 380 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.