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Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, April 2020
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1 X user

Citations

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22 Dimensions

Readers on

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21 Mendeley
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Title
Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization
Published in
Frontiers in Bioengineering and Biotechnology, April 2020
DOI 10.3389/fbioe.2020.00218
Pubmed ID
Authors

Feng Huang, Yang Qiu, Qiaojun Li, Shichao Liu, Fuchuan Ni

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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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Student > Master 4 19%
Researcher 2 10%
Student > Bachelor 1 5%
Unspecified 1 5%
Other 2 10%
Unknown 7 33%
Readers by discipline Count As %
Computer Science 7 33%
Biochemistry, Genetics and Molecular Biology 2 10%
Unspecified 1 5%
Environmental Science 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 8 38%
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 09 April 2020.
All research outputs
#20,612,116
of 23,201,298 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#4,709
of 6,883 outputs
Outputs of similar age
#319,308
of 373,780 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#291
of 354 outputs
Altmetric has tracked 23,201,298 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 6,883 research outputs from this source. They receive a mean Attention Score of 3.4. 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 373,780 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 354 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.