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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 (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
105 Dimensions

Readers on

mendeley
264 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

Mendeley readers

The data shown below were compiled from readership statistics for 264 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 255 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 54 20%
Student > Ph. D. Student 53 20%
Student > Bachelor 23 9%
Student > Master 21 8%
Student > Doctoral Student 11 4%
Other 36 14%
Unknown 66 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 19%
Biochemistry, Genetics and Molecular Biology 43 16%
Medicine and Dentistry 17 6%
Computer Science 17 6%
Chemistry 14 5%
Other 43 16%
Unknown 80 30%
Attention Score in Context

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
#8,759,452
of 25,837,817 outputs
Outputs from Genome Medicine
#1,257
of 1,611 outputs
Outputs of similar age
#81,788
of 241,410 outputs
Outputs of similar age from Genome Medicine
#9
of 25 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,611 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. 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 241,410 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 52% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.