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Selecting precise reference normal tissue samples for cancer research using a deep learning approach

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

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
facebook
1 Facebook page

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
30 Mendeley
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Title
Selecting precise reference normal tissue samples for cancer research using a deep learning approach
Published in
BMC Medical Genomics, January 2019
DOI 10.1186/s12920-018-0463-6
Pubmed ID
Authors

William Z. D. Zeng, Benjamin S. Glicksberg, Yangyan Li, Bin Chen

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Master 4 13%
Researcher 4 13%
Student > Bachelor 4 13%
Student > Doctoral Student 2 7%
Other 2 7%
Unknown 7 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 17%
Biochemistry, Genetics and Molecular Biology 5 17%
Computer Science 4 13%
Medicine and Dentistry 3 10%
Philosophy 1 3%
Other 3 10%
Unknown 9 30%

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 17 February 2019.
All research outputs
#12,698,323
of 16,638,522 outputs
Outputs from BMC Medical Genomics
#612
of 880 outputs
Outputs of similar age
#232,441
of 343,303 outputs
Outputs of similar age from BMC Medical Genomics
#10
of 23 outputs
Altmetric has tracked 16,638,522 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 880 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 25th percentile – i.e., 25% 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 343,303 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 23 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.