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A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features

Overview of attention for article published in Nature Machine Intelligence, August 2024
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 851)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
26 news outlets
blogs
4 blogs
twitter
64 X users
facebook
1 Facebook page

Readers on

mendeley
7 Mendeley
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Title
A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features
Published in
Nature Machine Intelligence, August 2024
DOI 10.1038/s42256-024-00883-x
Pubmed ID
Authors

Davide Carnevali, Limei Zhong, Esther González-Almela, Carlotta Viana, Mikhail Rotkevich, Aiping Wang, Daniel Franco-Barranco, Aitor Gonzalez-Marfil, Maria Victoria Neguembor, Alvaro Castells-Garcia, Ignacio Arganda-Carreras, Maria Pia Cosma

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 64 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 29%
Professor 1 14%
Professor > Associate Professor 1 14%
Researcher 1 14%
Student > Postgraduate 1 14%
Other 0 0%
Unknown 1 14%
Readers by discipline Count As %
Unspecified 2 29%
Biochemistry, Genetics and Molecular Biology 1 14%
Mathematics 1 14%
Agricultural and Biological Sciences 1 14%
Materials Science 1 14%
Other 0 0%
Unknown 1 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 238. 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 03 October 2024.
All research outputs
#171,312
of 26,787,735 outputs
Outputs from Nature Machine Intelligence
#44
of 851 outputs
Outputs of similar age
#1,839
of 274,790 outputs
Outputs of similar age from Nature Machine Intelligence
#1
of 38 outputs
Altmetric has tracked 26,787,735 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 851 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 62.8. This one has done particularly well, scoring higher than 94% 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 274,790 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.