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Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Overview of attention for article published in BMC Genomics, September 2016
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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 (75th percentile)

Mentioned by

twitter
7 tweeters

Citations

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66 Dimensions

Readers on

mendeley
107 Mendeley
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Title
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons
Published in
BMC Genomics, September 2016
DOI 10.1186/s12864-016-2889-6
Pubmed ID
Authors

Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, Mario Marchand, Michael Tyers, Vivian G. Loo, Anne-Marie Bourgault, François Laviolette, Jacques Corbeil

Abstract

The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery. Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes ( http://github.com/aldro61/kover/ ).

Twitter Demographics

The data shown below were collected from the profiles of 7 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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 2%
France 1 <1%
Unknown 104 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 21%
Student > Master 21 20%
Researcher 20 19%
Student > Bachelor 9 8%
Student > Postgraduate 5 5%
Other 10 9%
Unknown 20 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 25%
Biochemistry, Genetics and Molecular Biology 21 20%
Computer Science 11 10%
Immunology and Microbiology 7 7%
Medicine and Dentistry 5 5%
Other 12 11%
Unknown 24 22%

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 06 November 2016.
All research outputs
#3,308,142
of 12,321,517 outputs
Outputs from BMC Genomics
#2,043
of 7,222 outputs
Outputs of similar age
#84,121
of 264,508 outputs
Outputs of similar age from BMC Genomics
#65
of 265 outputs
Altmetric has tracked 12,321,517 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,222 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 70% 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 264,508 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 265 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.