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Algorithm discovery by protein folding game players

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, November 2011
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Citations

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411 Dimensions

Readers on

mendeley
645 Mendeley
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22 CiteULike
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Title
Algorithm discovery by protein folding game players
Published in
Proceedings of the National Academy of Sciences of the United States of America, November 2011
DOI 10.1073/pnas.1115898108
Pubmed ID
Authors

Firas Khatib, Seth Cooper, Michael D. Tyka, Kefan Xu, Ilya Makedon, Zoran Popović, David Baker, Foldit Players

Abstract

Foldit is a multiplayer online game in which players collaborate and compete to create accurate protein structure models. For specific hard problems, Foldit player solutions can in some cases outperform state-of-the-art computational methods. However, very little is known about how collaborative gameplay produces these results and whether Foldit player strategies can be formalized and structured so that they can be used by computers. To determine whether high performing player strategies could be collectively codified, we augmented the Foldit gameplay mechanics with tools for players to encode their folding strategies as "recipes" and to share their recipes with other players, who are able to further modify and redistribute them. Here we describe the rapid social evolution of player-developed folding algorithms that took place in the year following the introduction of these tools. Players developed over 5,400 different recipes, both by creating new algorithms and by modifying and recombining successful recipes developed by other players. The most successful recipes rapidly spread through the Foldit player population, and two of the recipes became particularly dominant. Examination of the algorithms encoded in these two recipes revealed a striking similarity to an unpublished algorithm developed by scientists over the same period. Benchmark calculations show that the new algorithm independently discovered by scientists and by Foldit players outperforms previously published methods. Thus, online scientific game frameworks have the potential not only to solve hard scientific problems, but also to discover and formalize effective new strategies and algorithms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 53 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 645 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 4%
Germany 13 2%
United Kingdom 12 2%
Canada 6 <1%
Brazil 5 <1%
France 3 <1%
Switzerland 2 <1%
Spain 2 <1%
Japan 2 <1%
Other 16 2%
Unknown 556 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 157 24%
Researcher 132 20%
Student > Master 74 11%
Student > Bachelor 67 10%
Professor 33 5%
Other 124 19%
Unknown 58 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 135 21%
Computer Science 125 19%
Biochemistry, Genetics and Molecular Biology 78 12%
Chemistry 44 7%
Social Sciences 32 5%
Other 167 26%
Unknown 64 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 149. 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 13 December 2022.
All research outputs
#276,424
of 25,403,829 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#5,116
of 103,028 outputs
Outputs of similar age
#996
of 154,557 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#29
of 751 outputs
Altmetric has tracked 25,403,829 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 103,028 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.4. This one has done particularly well, scoring higher than 95% 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 154,557 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 751 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 96% of its contemporaries.