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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 (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
19 X users

Citations

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41 Dimensions

Readers on

mendeley
68 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

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 18%
Researcher 10 15%
Student > Ph. D. Student 10 15%
Student > Bachelor 7 10%
Professor > Associate Professor 3 4%
Other 5 7%
Unknown 21 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 25%
Agricultural and Biological Sciences 9 13%
Medicine and Dentistry 6 9%
Computer Science 5 7%
Engineering 4 6%
Other 6 9%
Unknown 21 31%
Attention Score in Context

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
#2,620,404
of 23,197,711 outputs
Outputs from Genome Medicine
#599
of 1,452 outputs
Outputs of similar age
#58,960
of 359,239 outputs
Outputs of similar age from Genome Medicine
#13
of 25 outputs
Altmetric has tracked 23,197,711 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 1,452 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one has gotten more attention than average, scoring higher than 58% 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 359,239 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 83% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.