↓ Skip to main content

Antimicrobial Resistance Prediction in PATRIC and RAST

Overview of attention for article published in Scientific Reports, June 2016
Altmetric Badge

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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
21 tweeters

Citations

dimensions_citation
41 Dimensions

Readers on

mendeley
156 Mendeley
citeulike
1 CiteULike
Title
Antimicrobial Resistance Prediction in PATRIC and RAST
Published in
Scientific Reports, June 2016
DOI 10.1038/srep27930
Pubmed ID
Authors

James J. Davis, Sébastien Boisvert, Thomas Brettin, Ronald W. Kenyon, Chunhong Mao, Robert Olson, Ross Overbeek, John Santerre, Maulik Shukla, Alice R. Wattam, Rebecca Will, Fangfang Xia, Rick Stevens

Abstract

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88-99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71-88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Switzerland 1 <1%
France 1 <1%
Malaysia 1 <1%
Denmark 1 <1%
Unknown 151 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 22%
Researcher 33 21%
Student > Master 25 16%
Other 15 10%
Unspecified 14 9%
Other 35 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 24%
Biochemistry, Genetics and Molecular Biology 34 22%
Unspecified 21 13%
Computer Science 16 10%
Medicine and Dentistry 13 8%
Other 34 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 19 May 2018.
All research outputs
#1,330,033
of 12,963,687 outputs
Outputs from Scientific Reports
#11,108
of 61,398 outputs
Outputs of similar age
#37,472
of 263,655 outputs
Outputs of similar age from Scientific Reports
#560
of 3,029 outputs
Altmetric has tracked 12,963,687 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 61,398 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.4. This one has done well, scoring higher than 81% 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 263,655 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 85% of its contemporaries.
We're also able to compare this research output to 3,029 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.