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Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease

Overview of attention for article published in Microbiome, January 2018
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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 (91st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

blogs
1 blog
twitter
27 tweeters

Citations

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69 Dimensions

Readers on

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156 Mendeley
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Title
Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease
Published in
Microbiome, January 2018
DOI 10.1186/s40168-018-0398-3
Pubmed ID
Authors

Gavin M. Douglas, Richard Hansen, Casey M. A. Jones, Katherine A. Dunn, André M. Comeau, Joseph P. Bielawski, Rachel Tayler, Emad M. El-Omar, Richard K. Russell, Georgina L. Hold, Morgan G. I. Langille, Johan Van Limbergen

Abstract

Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples. We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response. We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.

Twitter Demographics

The data shown below were collected from the profiles of 27 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 %
Unknown 156 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 29 19%
Student > Ph. D. Student 25 16%
Researcher 24 15%
Student > Bachelor 11 7%
Student > Doctoral Student 8 5%
Other 22 14%
Unknown 37 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 30 19%
Medicine and Dentistry 26 17%
Agricultural and Biological Sciences 21 13%
Computer Science 12 8%
Immunology and Microbiology 8 5%
Other 15 10%
Unknown 44 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 February 2020.
All research outputs
#1,238,311
of 20,402,729 outputs
Outputs from Microbiome
#456
of 1,232 outputs
Outputs of similar age
#43,848
of 490,422 outputs
Outputs of similar age from Microbiome
#69
of 146 outputs
Altmetric has tracked 20,402,729 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,232 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.3. This one has gotten more attention than average, scoring higher than 62% 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 490,422 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 146 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 53% of its contemporaries.