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Heterogeneous network embedding enabling accurate disease association predictions

Overview of attention for article published in BMC Medical Genomics, December 2019
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Heterogeneous network embedding enabling accurate disease association predictions
Published in
BMC Medical Genomics, December 2019
DOI 10.1186/s12920-019-0623-3
Pubmed ID
Authors

Yun Xiong, Mengjie Guo, Lu Ruan, Xiangnan Kong, Chunlei Tang, Yangyong Zhu, Wei Wang

Twitter Demographics

The data shown below were collected from the profiles of 2 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Student > Bachelor 4 15%
Researcher 4 15%
Student > Master 3 12%
Professor 1 4%
Other 1 4%
Unknown 7 27%
Readers by discipline Count As %
Computer Science 6 23%
Business, Management and Accounting 3 12%
Biochemistry, Genetics and Molecular Biology 3 12%
Agricultural and Biological Sciences 3 12%
Engineering 2 8%
Other 0 0%
Unknown 9 35%

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 04 January 2020.
All research outputs
#10,193,600
of 16,540,864 outputs
Outputs from BMC Medical Genomics
#469
of 873 outputs
Outputs of similar age
#212,862
of 383,449 outputs
Outputs of similar age from BMC Medical Genomics
#48
of 114 outputs
Altmetric has tracked 16,540,864 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 873 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 40th percentile – i.e., 40% 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 383,449 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.