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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 (#46 of 8,348)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
86 news outlets
blogs
4 blogs
twitter
129 tweeters
wikipedia
3 Wikipedia pages
reddit
1 Redditor

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
46 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

Twitter Demographics

The data shown below were collected from the profiles of 129 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 35%
Student > Bachelor 7 15%
Student > Ph. D. Student 6 13%
Other 3 7%
Unspecified 3 7%
Other 6 13%
Unknown 5 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 35%
Agricultural and Biological Sciences 6 13%
Unspecified 5 11%
Neuroscience 3 7%
Engineering 3 7%
Other 6 13%
Unknown 7 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 717. 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 March 2023.
All research outputs
#24,266
of 23,419,482 outputs
Outputs from Nature Biotechnology
#46
of 8,348 outputs
Outputs of similar age
#670
of 413,948 outputs
Outputs of similar age from Nature Biotechnology
#2
of 119 outputs
Altmetric has tracked 23,419,482 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 8,348 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 41.4. 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 413,948 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 98% of its contemporaries.