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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

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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7 X users

Citations

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96 Dimensions

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179 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/ ).

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 179 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 2%
France 1 <1%
Unknown 175 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 40 22%
Researcher 35 20%
Student > Ph. D. Student 30 17%
Student > Bachelor 13 7%
Student > Postgraduate 6 3%
Other 19 11%
Unknown 36 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 25%
Biochemistry, Genetics and Molecular Biology 32 18%
Computer Science 23 13%
Immunology and Microbiology 11 6%
Medicine and Dentistry 7 4%
Other 19 11%
Unknown 42 23%
Attention Score in Context

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 23 July 2023.
All research outputs
#7,769,975
of 24,137,435 outputs
Outputs from BMC Genomics
#3,589
of 10,915 outputs
Outputs of similar age
#113,834
of 327,580 outputs
Outputs of similar age from BMC Genomics
#94
of 284 outputs
Altmetric has tracked 24,137,435 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 10,915 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 65% 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 327,580 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 64% of its contemporaries.
We're also able to compare this research output to 284 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 65% of its contemporaries.