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Inferring microbial interaction network from microbiome data using RMN algorithm

Overview of attention for article published in BMC Systems Biology, September 2015
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  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

5 tweeters
1 Google+ user


12 Dimensions

Readers on

103 Mendeley
5 CiteULike
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Inferring microbial interaction network from microbiome data using RMN algorithm
Published in
BMC Systems Biology, September 2015
DOI 10.1186/s12918-015-0199-2
Pubmed ID

Kun-Nan Tsai, Shu-Hsi Lin, Wei-Chung Liu, Daryi Wang


Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis. In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI. The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Canada 2 2%
Russia 1 <1%
Japan 1 <1%
Ireland 1 <1%
Unknown 95 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 22%
Student > Ph. D. Student 22 21%
Student > Bachelor 12 12%
Student > Master 11 11%
Professor > Associate Professor 6 6%
Other 13 13%
Unknown 16 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 44%
Biochemistry, Genetics and Molecular Biology 10 10%
Environmental Science 8 8%
Engineering 6 6%
Computer Science 4 4%
Other 12 12%
Unknown 18 17%

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 21 September 2016.
All research outputs
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Outputs from BMC Systems Biology
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Outputs of similar age
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Outputs of similar age from BMC Systems Biology
of 3 outputs
Altmetric has tracked 15,918,484 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,107 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% 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 241,624 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 70% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them