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ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

Overview of attention for article published in BMC Bioinformatics, July 2021
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Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
6 Mendeley
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Title
ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations
Published in
BMC Bioinformatics, July 2021
DOI 10.1186/s12859-021-04266-6
Authors

Xue-Jun Chen, Xin-Yun Hua, Zhen-Ran Jiang

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 50%
Unspecified 1 17%
Researcher 1 17%
Student > Doctoral Student 1 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 33%
Biochemistry, Genetics and Molecular Biology 1 17%
Unspecified 1 17%
Computer Science 1 17%
Engineering 1 17%
Other 0 0%

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 04 July 2021.
All research outputs
#16,733,755
of 21,474,792 outputs
Outputs from BMC Bioinformatics
#5,668
of 6,968 outputs
Outputs of similar age
#232,476
of 341,536 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 19 outputs
Altmetric has tracked 21,474,792 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 6,968 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 13th percentile – i.e., 13% 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 341,536 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.