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SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

Overview of attention for article published in BMC Research Notes, March 2021
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Mentioned by

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1 X user

Citations

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2 Dimensions

Readers on

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14 Mendeley
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Title
SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models
Published in
BMC Research Notes, March 2021
DOI 10.1186/s13104-021-05518-7
Pubmed ID
Authors

Yupeng Wang, Rosario B. Jaime-Lara, Abhrarup Roy, Ying Sun, Xinyue Liu, Paule V. Joseph

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 21%
Researcher 2 14%
Student > Master 2 14%
Student > Bachelor 1 7%
Student > Doctoral Student 1 7%
Other 0 0%
Unknown 5 36%
Readers by discipline Count As %
Computer Science 2 14%
Agricultural and Biological Sciences 2 14%
Biochemistry, Genetics and Molecular Biology 1 7%
Nursing and Health Professions 1 7%
Medicine and Dentistry 1 7%
Other 1 7%
Unknown 6 43%
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 20 March 2021.
All research outputs
#19,017,658
of 23,577,761 outputs
Outputs from BMC Research Notes
#3,045
of 4,305 outputs
Outputs of similar age
#320,865
of 427,961 outputs
Outputs of similar age from BMC Research Notes
#50
of 75 outputs
Altmetric has tracked 23,577,761 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 4,305 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 16th percentile – i.e., 16% 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 427,961 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 75 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.