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Predicting tumor cell line response to drug pairs with deep learning

Overview of attention for article published in BMC Bioinformatics, December 2018
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Mentioned by

twitter
1 tweeter

Citations

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25 Dimensions

Readers on

mendeley
67 Mendeley
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Title
Predicting tumor cell line response to drug pairs with deep learning
Published in
BMC Bioinformatics, December 2018
DOI 10.1186/s12859-018-2509-3
Authors

Fangfang Xia, Maulik Shukla, Thomas Brettin, Cristina Garcia-Cardona, Judith Cohn, Jonathan E. Allen, Sergei Maslov, Susan L. Holbeck, James H. Doroshow, Yvonne A. Evrard, Eric A. Stahlberg, Rick L. Stevens

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Researcher 12 18%
Student > Bachelor 8 12%
Student > Master 6 9%
Other 5 7%
Other 6 9%
Unknown 17 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 21%
Computer Science 12 18%
Engineering 4 6%
Agricultural and Biological Sciences 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Other 10 15%
Unknown 20 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 December 2018.
All research outputs
#12,432,541
of 14,058,698 outputs
Outputs from BMC Bioinformatics
#4,861
of 5,272 outputs
Outputs of similar age
#312,682
of 368,075 outputs
Outputs of similar age from BMC Bioinformatics
#373
of 414 outputs
Altmetric has tracked 14,058,698 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,272 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% 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 368,075 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 414 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.