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Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma

Overview of attention for article published in Blood Cancer Journal, May 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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10 X users

Citations

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25 Dimensions

Readers on

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50 Mendeley
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Title
Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
Published in
Blood Cancer Journal, May 2020
DOI 10.1038/s41408-020-0322-5
Pubmed ID
Authors

Victor Bobée, Fanny Drieux, Vinciane Marchand, Vincent Sater, Liana Veresezan, Jean-Michel Picquenot, Pierre-Julien Viailly, Marie-Delphine Lanic, Mathieu Viennot, Elodie Bohers, Lucie Oberic, Christiane Copie-Bergman, Thierry Jo Molina, Philippe Gaulard, Corinne Haioun, Gilles Salles, Hervé Tilly, Fabrice Jardin, Philippe Ruminy

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 18%
Researcher 7 14%
Other 6 12%
Student > Master 6 12%
Student > Ph. D. Student 4 8%
Other 6 12%
Unknown 12 24%
Readers by discipline Count As %
Medicine and Dentistry 12 24%
Biochemistry, Genetics and Molecular Biology 11 22%
Computer Science 6 12%
Nursing and Health Professions 2 4%
Agricultural and Biological Sciences 2 4%
Other 5 10%
Unknown 12 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 June 2020.
All research outputs
#4,670,184
of 23,208,901 outputs
Outputs from Blood Cancer Journal
#426
of 1,118 outputs
Outputs of similar age
#111,222
of 390,576 outputs
Outputs of similar age from Blood Cancer Journal
#19
of 33 outputs
Altmetric has tracked 23,208,901 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,118 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one has gotten more attention than average, scoring higher than 61% 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 390,576 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 71% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.