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A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

Overview of attention for article published in Genome Medicine, July 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
76 Dimensions

Readers on

mendeley
216 Mendeley
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Title
A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening
Published in
Genome Medicine, July 2014
DOI 10.1186/s13073-014-0057-7
Pubmed ID
Authors

Jouhyun Jeon, Satra Nim, Joan Teyra, Alessandro Datti, Jeffrey L Wrana, Sachdev S Sidhu, Jason Moffat, Philip M Kim

Mendeley readers

The data shown below were compiled from readership statistics for 216 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Germany 1 <1%
Switzerland 1 <1%
Brazil 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
India 1 <1%
Unknown 207 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 23%
Researcher 48 22%
Student > Master 20 9%
Student > Bachelor 19 9%
Student > Doctoral Student 9 4%
Other 30 14%
Unknown 41 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 22%
Biochemistry, Genetics and Molecular Biology 38 18%
Computer Science 18 8%
Medicine and Dentistry 14 6%
Chemistry 11 5%
Other 33 15%
Unknown 54 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 June 2021.
All research outputs
#6,925,851
of 21,353,399 outputs
Outputs from Genome Medicine
#1,057
of 1,355 outputs
Outputs of similar age
#128,577
of 339,196 outputs
Outputs of similar age from Genome Medicine
#1
of 1 outputs
Altmetric has tracked 21,353,399 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,355 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.9. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 339,196 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them