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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 (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
105 Dimensions

Readers on

mendeley
262 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 262 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 253 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 54 21%
Student > Ph. D. Student 52 20%
Student > Bachelor 23 9%
Student > Master 21 8%
Student > Doctoral Student 11 4%
Other 35 13%
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 13 5%
Other 42 16%
Unknown 80 31%
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
#7,656,930
of 23,310,485 outputs
Outputs from Genome Medicine
#1,148
of 1,456 outputs
Outputs of similar age
#74,210
of 229,966 outputs
Outputs of similar age from Genome Medicine
#9
of 24 outputs
Altmetric has tracked 23,310,485 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 1,456 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. 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 229,966 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 55% of its contemporaries.
We're also able to compare this research output to 24 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 54% of its contemporaries.