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iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework

Overview of attention for article published in BMC Bioinformatics, November 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 X users

Citations

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5 Dimensions

Readers on

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5 Mendeley
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Title
iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework
Published in
BMC Bioinformatics, November 2022
DOI 10.1186/s12859-022-05033-x
Pubmed ID
Authors

Meng Liao, Jian-ping Zhao, Jing Tian, Chun-Hou Zheng

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 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 %
Student > Master 1 20%
Unknown 4 80%
Readers by discipline Count As %
Social Sciences 1 20%
Unknown 4 80%
Attention Score in Context

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 20 November 2022.
All research outputs
#15,379,405
of 24,843,842 outputs
Outputs from BMC Bioinformatics
#4,683
of 7,595 outputs
Outputs of similar age
#200,205
of 426,617 outputs
Outputs of similar age from BMC Bioinformatics
#86
of 161 outputs
Altmetric has tracked 24,843,842 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,595 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 35th percentile – i.e., 35% 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 426,617 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.