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A universal deep-learning model for zinc finger design enables transcription factor reprogramming

Overview of attention for article published in Nature Biotechnology, January 2023
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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 (#50 of 8,659)
  • 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
89 news outlets
blogs
4 blogs
twitter
137 X users
facebook
1 Facebook page
wikipedia
4 Wikipedia pages
reddit
1 Redditor

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
135 Mendeley
Title
A universal deep-learning model for zinc finger design enables transcription factor reprogramming
Published in
Nature Biotechnology, January 2023
DOI 10.1038/s41587-022-01624-4
Pubmed ID
Authors

David M. Ichikawa, Osama Abdin, Nader Alerasool, Manjunatha Kogenaru, April L. Mueller, Han Wen, David O. Giganti, Gregory W. Goldberg, Samantha Adams, Jeffrey M. Spencer, Rozita Razavi, Satra Nim, Hong Zheng, Courtney Gionco, Finnegan T. Clark, Alexey Strokach, Timothy R. Hughes, Timothee Lionnet, Mikko Taipale, Philip M. Kim, Marcus B. Noyes

X Demographics

X Demographics

The data shown below were collected from the profiles of 137 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 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 135 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 21%
Student > Ph. D. Student 20 15%
Student > Master 10 7%
Student > Bachelor 9 7%
Other 8 6%
Other 16 12%
Unknown 43 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 43 32%
Agricultural and Biological Sciences 17 13%
Neuroscience 5 4%
Medicine and Dentistry 4 3%
Chemical Engineering 4 3%
Other 15 11%
Unknown 47 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 740. 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 15 December 2023.
All research outputs
#27,845
of 25,971,360 outputs
Outputs from Nature Biotechnology
#50
of 8,659 outputs
Outputs of similar age
#776
of 479,836 outputs
Outputs of similar age from Nature Biotechnology
#3
of 119 outputs
Altmetric has tracked 25,971,360 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 8,659 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.7. This one has done particularly well, scoring higher than 99% 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 479,836 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 119 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.