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Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC

Overview of attention for article published in Frontiers in Genetics, February 2022
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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10 X users

Citations

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3 Dimensions

Readers on

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21 Mendeley
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Title
Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC
Published in
Frontiers in Genetics, February 2022
DOI 10.3389/fgene.2021.806457
Pubmed ID
Authors

Ryan Liu, Emmanuel Dollinger, Qing Nie

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 14%
Student > Master 3 14%
Researcher 3 14%
Student > Ph. D. Student 2 10%
Unknown 10 48%
Readers by discipline Count As %
Computer Science 3 14%
Pharmacology, Toxicology and Pharmaceutical Science 2 10%
Engineering 2 10%
Agricultural and Biological Sciences 1 5%
Nursing and Health Professions 1 5%
Other 2 10%
Unknown 10 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 21 February 2022.
All research outputs
#7,084,016
of 23,172,045 outputs
Outputs from Frontiers in Genetics
#2,209
of 12,204 outputs
Outputs of similar age
#159,899
of 506,999 outputs
Outputs of similar age from Frontiers in Genetics
#95
of 826 outputs
Altmetric has tracked 23,172,045 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 12,204 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 81% 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 506,999 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 826 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.