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Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models

Overview of attention for article published in Genome Biology (Online Edition), September 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

blogs
1 blog
twitter
11 tweeters

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
65 Mendeley
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Title
Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models
Published in
Genome Biology (Online Edition), September 2016
DOI 10.1186/s13059-016-1050-9
Pubmed ID
Authors

Paul Geeleher, Nancy J. Cox, R. Stephanie Huang

Abstract

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 38%
Researcher 13 20%
Student > Master 6 9%
Student > Bachelor 3 5%
Student > Postgraduate 3 5%
Other 5 8%
Unknown 10 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 29%
Biochemistry, Genetics and Molecular Biology 17 26%
Computer Science 8 12%
Medicine and Dentistry 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Other 3 5%
Unknown 11 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 17 February 2017.
All research outputs
#1,547,616
of 16,560,800 outputs
Outputs from Genome Biology (Online Edition)
#1,473
of 3,486 outputs
Outputs of similar age
#35,430
of 271,195 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 16,560,800 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,486 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.3. This one has gotten more attention than average, scoring higher than 57% 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 271,195 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% 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