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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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Mentioned by

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1 X user
facebook
1 Facebook page

Citations

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39 Dimensions

Readers on

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102 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

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

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 16%
Researcher 15 15%
Student > Bachelor 10 10%
Student > Master 10 10%
Student > Doctoral Student 6 6%
Other 5 5%
Unknown 40 39%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 21%
Agricultural and Biological Sciences 9 9%
Medicine and Dentistry 8 8%
Computer Science 6 6%
Engineering 3 3%
Other 10 10%
Unknown 45 44%
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 17 February 2019.
All research outputs
#18,005,961
of 23,125,690 outputs
Outputs from BMC Medical Genomics
#805
of 1,238 outputs
Outputs of similar age
#304,684
of 437,790 outputs
Outputs of similar age from BMC Medical Genomics
#31
of 46 outputs
Altmetric has tracked 23,125,690 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 1,238 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% 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 437,790 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.