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A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

Overview of attention for article published in Genome Medicine, February 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

twitter
19 tweeters

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
59 Mendeley
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Title
A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles
Published in
Genome Medicine, February 2020
DOI 10.1186/s13073-020-0720-0
Pubmed ID
Authors

Li Wang, Robert P. Sebra, John P. Sfakianos, Kimaada Allette, Wenhui Wang, Seungyeul Yoo, Nina Bhardwaj, Eric E. Schadt, Xin Yao, Matthew D. Galsky, Jun Zhu

Twitter Demographics

The data shown below were collected from the profiles of 19 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 19%
Researcher 10 17%
Student > Ph. D. Student 9 15%
Professor > Associate Professor 3 5%
Student > Bachelor 3 5%
Other 5 8%
Unknown 18 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 24%
Agricultural and Biological Sciences 9 15%
Medicine and Dentistry 5 8%
Computer Science 4 7%
Engineering 3 5%
Other 6 10%
Unknown 18 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 2021.
All research outputs
#2,234,193
of 20,310,861 outputs
Outputs from Genome Medicine
#519
of 1,315 outputs
Outputs of similar age
#48,089
of 280,255 outputs
Outputs of similar age from Genome Medicine
#1
of 1 outputs
Altmetric has tracked 20,310,861 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,315 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.5. This one has gotten more attention than average, scoring higher than 60% of its peers.
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 280,255 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them