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Patient-derived models of acquired resistance can identify effective drug combinations for cancer

Overview of attention for article published in Science, November 2014
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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 (90th percentile)

Mentioned by

news
13 news outlets
blogs
2 blogs
twitter
83 X users
patent
23 patents
weibo
1 weibo user
facebook
5 Facebook pages
wikipedia
1 Wikipedia page
googleplus
2 Google+ users

Citations

dimensions_citation
639 Dimensions

Readers on

mendeley
594 Mendeley
citeulike
5 CiteULike
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Title
Patient-derived models of acquired resistance can identify effective drug combinations for cancer
Published in
Science, November 2014
DOI 10.1126/science.1254721
Pubmed ID
Authors

Adam S Crystal, Alice T Shaw, Lecia V Sequist, Luc Friboulet, Matthew J Niederst, Elizabeth L Lockerman, Rosa L Frias, Justin F Gainor, Arnaud Amzallag, Patricia Greninger, Dana Lee, Anuj Kalsy, Maria Gomez-Caraballo, Leila Elamine, Emily Howe, Wooyoung Hur, Eugene Lifshits, Hayley E Robinson, Ryohei Katayama, Anthony C Faber, Mark M Awad, Sridhar Ramaswamy, Mari Mino-Kenudson, A John Iafrate, Cyril H Benes, Jeffrey A Engelman

X Demographics

X Demographics

The data shown below were collected from the profiles of 83 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 594 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 1%
United Kingdom 6 1%
Japan 3 <1%
Germany 1 <1%
Hungary 1 <1%
Austria 1 <1%
Denmark 1 <1%
Switzerland 1 <1%
China 1 <1%
Other 3 <1%
Unknown 568 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 148 25%
Researcher 142 24%
Student > Bachelor 51 9%
Other 36 6%
Student > Master 34 6%
Other 99 17%
Unknown 84 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 166 28%
Biochemistry, Genetics and Molecular Biology 130 22%
Medicine and Dentistry 96 16%
Engineering 19 3%
Computer Science 15 3%
Other 59 10%
Unknown 109 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 171. 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 07 February 2024.
All research outputs
#243,644
of 25,863,888 outputs
Outputs from Science
#6,798
of 83,366 outputs
Outputs of similar age
#2,303
of 271,660 outputs
Outputs of similar age from Science
#78
of 819 outputs
Altmetric has tracked 25,863,888 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 83,366 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 66.1. This one has done particularly well, scoring higher than 91% 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 271,660 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 819 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 90% of its contemporaries.