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EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction

Overview of attention for article published in BMC Bioinformatics, March 2021
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  • Average Attention Score compared to outputs of the same age

Mentioned by

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5 X users

Citations

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22 Dimensions

Readers on

mendeley
25 Mendeley
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Title
EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction
Published in
BMC Bioinformatics, March 2021
DOI 10.1186/s12859-021-04069-9
Pubmed ID
Authors

Jingjing Wang, Yanpeng Zhao, Weikang Gong, Yang Liu, Mei Wang, Xiaoqian Huang, Jianjun Tan

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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 16%
Professor 3 12%
Student > Bachelor 2 8%
Researcher 2 8%
Lecturer 1 4%
Other 4 16%
Unknown 9 36%
Readers by discipline Count As %
Computer Science 6 24%
Biochemistry, Genetics and Molecular Biology 4 16%
Agricultural and Biological Sciences 2 8%
Unspecified 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 10 40%
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 21 March 2021.
All research outputs
#14,717,488
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#4,823
of 7,418 outputs
Outputs of similar age
#230,369
of 427,961 outputs
Outputs of similar age from BMC Bioinformatics
#131
of 168 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% 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 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 168 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.