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A method for automated pathogenic content estimation with application to rheumatoid arthritis

Overview of attention for article published in BMC Systems Biology, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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8 Dimensions

Readers on

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31 Mendeley
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Title
A method for automated pathogenic content estimation with application to rheumatoid arthritis
Published in
BMC Systems Biology, November 2016
DOI 10.1186/s12918-016-0344-6
Pubmed ID
Authors

Xiaoyuan Zhou, Christine Nardini

Abstract

Sequencing technologies applied to mammals' microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases' progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated. Typical metagenomic analyses are run to associate to a condition (disease, therapy, diet) a pool of bacteria, whose eubiotic/dysbiotic potential is assessed either by α-diversity, a measure of the varieties populating the microbiome, or by Firmicutes to Bacteroides ratio, associated to systemic inflammation, and finally by manual and direct inspection of bacteria's biological functions, when known. These approaches lead to results sometimes difficult to interpret in terms of the evolution towards a specific microbial composition, harmed by large areas of unknown. We propose to additionally evaluate a microbiome based on its global composition, by automatic annotation of pathogenic genera and statistical assessment of the net varied frequency of harmless versus harmful organisms. This application is intuitive, quantitative and computationally efficient and designed to cope with the currently incomplete species' functional knowledge. Our results, applied to human GI-microbiome data exemplify how this layer of information provides additional insights into treatments' impact on the GI microbiome, allowing to characterize a more physiologic effects of Prednisone versus Methotrexate, two treatments for rheumatoid arthritis (RA) a complex autoimmune systemic disease. Our quantitative analysis integrates with previous approaches offering an additional systemic level of interpretation here applied, for its potential to translate into clinically relevant information, to the therapies for RA.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 26%
Researcher 4 13%
Student > Ph. D. Student 4 13%
Student > Doctoral Student 2 6%
Student > Bachelor 2 6%
Other 4 13%
Unknown 7 23%
Readers by discipline Count As %
Medicine and Dentistry 8 26%
Agricultural and Biological Sciences 4 13%
Biochemistry, Genetics and Molecular Biology 2 6%
Psychology 2 6%
Linguistics 1 3%
Other 7 23%
Unknown 7 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 18 November 2016.
All research outputs
#1,259,662
of 8,650,672 outputs
Outputs from BMC Systems Biology
#70
of 874 outputs
Outputs of similar age
#59,086
of 295,956 outputs
Outputs of similar age from BMC Systems Biology
#4
of 17 outputs
Altmetric has tracked 8,650,672 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 874 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 91% 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 295,956 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 79% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.