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Unearthing new genomic markers of drug response by improved measurement of discriminative power

Overview of attention for article published in BMC Medical Genomics, February 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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8 tweeters

Citations

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2 Dimensions

Readers on

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23 Mendeley
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Title
Unearthing new genomic markers of drug response by improved measurement of discriminative power
Published in
BMC Medical Genomics, February 2018
DOI 10.1186/s12920-018-0336-z
Pubmed ID
Authors

Cuong C. Dang, Antonio Peón, Pedro J. Ballester

Abstract

Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking. Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers. The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium. Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS_FLI1 (sensitivity).

Twitter Demographics

The data shown below were collected from the profiles of 8 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 9%
United Kingdom 1 4%
Unknown 20 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 30%
Student > Ph. D. Student 5 22%
Student > Master 4 17%
Student > Doctoral Student 2 9%
Unspecified 1 4%
Other 3 13%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 35%
Computer Science 6 26%
Agricultural and Biological Sciences 3 13%
Chemistry 2 9%
Mathematics 1 4%
Other 2 9%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 October 2018.
All research outputs
#4,293,783
of 15,220,640 outputs
Outputs from BMC Medical Genomics
#215
of 798 outputs
Outputs of similar age
#115,972
of 363,220 outputs
Outputs of similar age from BMC Medical Genomics
#3
of 10 outputs
Altmetric has tracked 15,220,640 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 798 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 72% 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 363,220 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 67% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.