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Microbial "social networks"

Overview of attention for article published in BMC Genomics, November 2015
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
15 X users
patent
1 patent

Citations

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55 Dimensions

Readers on

mendeley
111 Mendeley
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Title
Microbial "social networks"
Published in
BMC Genomics, November 2015
DOI 10.1186/1471-2164-16-s11-s6
Pubmed ID
Authors

Mitch Fernandez, Juan D Riveros, Michael Campos, Kalai Mathee, Giri Narasimhan

Abstract

It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another. We develop tools and techniques that help us to investigate the nature of social interactions in microbial communities, and demonstrate ways of compactly capturing extensive information about these networks and visually conveying them in an effective manner. We define the concept of bacterial "social clubs", which are groups of taxa that tend to appear together in many samples. More importantly, we define the concept of "rival clubs", entire groups that tend to avoid occurring together in many samples. We show how to efficiently compute social clubs and rival clubs and demonstrate their utility with the help of examples including a smokers' dataset and a dataset from the Human Microbiome Project (HMP). The tools developed provide a framework for analyzing relationships between bacterial taxa modeled as bacterial co-occurrence networks. The computational techniques also provide a framework for identifying clubs and rival clubs and for studying differences in the microbiomes (and their interactions) of two or more collections of samples. Microbial relationships are similar to those found in social networks. In this work, we assume that strong (positive or negative) tendencies to co-occur or co-infect is likely to have biological, physiological, or ecological significance, possibly as a result of cooperation or competition. As a consequence of the analysis, a variety of biological interpretations are conjectured. In the human microbiome context, the pattern of strength of interactions between bacterial taxa is unique to body site.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Japan 1 <1%
Germany 1 <1%
Canada 1 <1%
Unknown 106 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 30%
Researcher 18 16%
Student > Master 13 12%
Student > Bachelor 10 9%
Student > Postgraduate 7 6%
Other 15 14%
Unknown 15 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 40%
Biochemistry, Genetics and Molecular Biology 13 12%
Computer Science 7 6%
Engineering 6 5%
Medicine and Dentistry 5 5%
Other 19 17%
Unknown 17 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 13 December 2023.
All research outputs
#1,874,291
of 25,436,226 outputs
Outputs from BMC Genomics
#402
of 11,268 outputs
Outputs of similar age
#26,576
of 294,466 outputs
Outputs of similar age from BMC Genomics
#15
of 391 outputs
Altmetric has tracked 25,436,226 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,268 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 96% 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 294,466 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 90% of its contemporaries.
We're also able to compare this research output to 391 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.