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Opportunities and obstacles for deep learning in biology and medicine

Overview of attention for article published in bioRxiv
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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 (99th percentile)

Citations

dimensions_citation
85 Dimensions

Readers on

mendeley
1228 Mendeley
citeulike
3 CiteULike
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Title
Opportunities and obstacles for deep learning in biology and medicine
Published in
bioRxiv
DOI 10.1101/142760
Authors

Ching, Travers, Himmelstein, Daniel S., Beaulieu-Jones, Brett K., Kalinin, Alexandr A., Do, Brian T., Way, Gregory P., Ferrero, Enrico, Agapow, Paul-Michael, Zietz, Michael, Hoffman, Michael M., Xie, Wei, Rosen, Gail L., Lengerich, Benjamin J., Israeli, Johnny, Lanchantin, Jack, Woloszynek, Stephen, Carpenter, Anne E., Shrikumar, Avanti, Xu, Jinbo, Cofer, Evan M., Lavender, Christopher A., Turaga, Srinivas C., Alexandari, Amr M., Lu, Zhiyong, Harris, David J., DeCaprio, Dave, Qi, Yanjun, Kundaje, Anshul, Peng, Yifan, Wiley, Laura K., Segler, Marwin H.S., Boca, Simina M., Swamidass, S. Joshua, Huang, Austin, Gitter, Anthony, Greene, Casey S.

Twitter Demographics

The data shown below were collected from the profiles of 852 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 1,228 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 <1%
United Kingdom 2 <1%
United States 2 <1%
Spain 1 <1%
Uruguay 1 <1%
Unknown 1220 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 222 18%
Researcher 203 17%
Student > Master 117 10%
Student > Bachelor 69 6%
Other 54 4%
Other 145 12%
Unknown 418 34%
Readers by discipline Count As %
Computer Science 240 20%
Biochemistry, Genetics and Molecular Biology 144 12%
Agricultural and Biological Sciences 141 11%
Engineering 72 6%
Medicine and Dentistry 46 4%
Other 147 12%
Unknown 438 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 545. 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 26 April 2022.
All research outputs
#35,202
of 22,560,481 outputs
Outputs from bioRxiv
#317
of 176,007 outputs
Outputs of similar age
#745
of 290,506 outputs
Outputs of similar age from bioRxiv
#2
of 2,813 outputs
Altmetric has tracked 22,560,481 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 176,007 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. 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 290,506 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 2,813 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.