↓ Skip to main content

Large language models generate functional protein sequences across diverse families

Overview of attention for article published in Nature Biotechnology, January 2023
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#26 of 8,648)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
60 news outlets
blogs
8 blogs
twitter
1166 X users
facebook
3 Facebook pages
wikipedia
4 Wikipedia pages
reddit
8 Redditors

Citations

dimensions_citation
286 Dimensions

Readers on

mendeley
551 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Large language models generate functional protein sequences across diverse families
Published in
Nature Biotechnology, January 2023
DOI 10.1038/s41587-022-01618-2
Pubmed ID
Authors

Ali Madani, Ben Krause, Eric R. Greene, Subu Subramanian, Benjamin P. Mohr, James M. Holton, Jose Luis Olmos, Caiming Xiong, Zachary Z. Sun, Richard Socher, James S. Fraser, Nikhil Naik

X Demographics

X Demographics

The data shown below were collected from the profiles of 1,166 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 551 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 551 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 97 18%
Researcher 92 17%
Student > Bachelor 33 6%
Student > Master 30 5%
Other 25 5%
Other 67 12%
Unknown 207 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 127 23%
Computer Science 52 9%
Agricultural and Biological Sciences 50 9%
Chemistry 24 4%
Engineering 15 3%
Other 66 12%
Unknown 217 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1106. 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 May 2024.
All research outputs
#14,055
of 25,936,091 outputs
Outputs from Nature Biotechnology
#26
of 8,648 outputs
Outputs of similar age
#421
of 479,346 outputs
Outputs of similar age from Nature Biotechnology
#1
of 119 outputs
Altmetric has tracked 25,936,091 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 8,648 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 479,346 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 99% of its contemporaries.