The data shown below were collected from the profiles of 135 X users who shared this research output. Click here to find out more about how the information was compiled.
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| 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
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 15 | 11% |
| United Kingdom | 2 | 1% |
| South Africa | 2 | 1% |
| Spain | 2 | 1% |
| Germany | 2 | 1% |
| Japan | 1 | <1% |
| Netherlands | 1 | <1% |
| Congo, The Democratic Republic of the | 1 | <1% |
| China | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 105 | 78% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 97 | 72% |
| Scientists | 35 | 26% |
| Science communicators (journalists, bloggers, editors) | 2 | 1% |
| Practitioners (doctors, other healthcare professionals) | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 146 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 21% |
| Student > Ph. D. Student | 23 | 16% |
| Student > Master | 10 | 7% |
| Student > Bachelor | 10 | 7% |
| Other | 8 | 5% |
| Other | 16 | 11% |
| Unknown | 49 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 45 | 31% |
| Agricultural and Biological Sciences | 18 | 12% |
| Neuroscience | 5 | 3% |
| Medicine and Dentistry | 4 | 3% |
| Chemical Engineering | 4 | 3% |
| Other | 17 | 12% |
| Unknown | 53 | 36% |
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
#28,936
of 26,567,854 outputs
Outputs from Nature Biotechnology
#50
of 8,784 outputs
Outputs of similar age
#820
of 493,007 outputs
Outputs of similar age from Nature Biotechnology
#3
of 119 outputs
Altmetric has tracked 26,567,854 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 8,784 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.8. 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 493,007 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.