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Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network

Overview of attention for article published in BMC Medical Genomics, October 2020
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1 X user

Citations

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9 Dimensions

Readers on

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9 Mendeley
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Title
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network
Published in
BMC Medical Genomics, October 2020
DOI 10.1186/s12920-020-00783-0
Pubmed ID
Authors

Minghui Liu, Jingyi Yang, Jiacheng Wang, Lei Deng

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

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Unknown 6 67%
Readers by discipline Count As %
Computer Science 2 22%
Biochemistry, Genetics and Molecular Biology 1 11%
Unknown 6 67%
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 24 October 2020.
All research outputs
#20,658,463
of 23,253,955 outputs
Outputs from BMC Medical Genomics
#1,021
of 1,245 outputs
Outputs of similar age
#358,736
of 419,872 outputs
Outputs of similar age from BMC Medical Genomics
#35
of 43 outputs
Altmetric has tracked 23,253,955 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,245 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 419,872 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.