ArticleBMC systems biology2015
Inferring microbial interaction network from microbiome data using RMN algorithm.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Establishing community structure and diversity within hydrothermal vent bacterial communities of the East Pacific Rise at 9°50'N.Microbiology spectrum · 2026Article
- Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks.Scientific reports · 2022Article
- Modeling Microbial Community Networks: Methods and Tools.Current genomics · 2021Review
- INFORMATION CONTENT OF HIGH-ORDER ASSOCIATIONS OF THE HUMAN GUT MICROBIOTA NETWORK.The annals of applied statistics · 2021Article
- Identifying prognostic pairwise relationships among bacterial species in microbiome studies.PLoS computational biology · 2021Article
- TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction.Bioinformatics (Oxford, England) · 2020Article
- IMPARO: inferring microbial interactions through parameter optimisation.BMC molecular and cell biology · 2020Article
- Probiotic Cocktail Identified by Microbial Network Analysis Inhibits Growth, Virulence Gene Expression, and Host Cell Colonization of Vancomycin-Resistant Enterococci.Microorganisms · 2020Article
- Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module.Evolutionary bioinformatics online · 2020Article
- Massively parallel screening of synthetic microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Fine-Scale Biogeography and the Inference of Ecological Interactions Among Neutrophilic Iron-Oxidizing Zetaproteobacteria as Determined by a Rule-Based Microbial Network.Frontiers in microbiology · 2019Article
- Comparison of Microbiota Variation in Korean Healthy Adolescents with Adults Suggests Notable Maturity Differences.Omics : a journal of integrative biology · 2018Article
- Interaction of genotype and diet on small intestine microbiota of Japanese quail fed a cholesterol enriched diet.Scientific reports · 2018Article
- MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles.BMC bioinformatics · 2016Article
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Authors and funding
4 authors.
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Abstract
backgroundMicrobial 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.
resultsIn 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.
conclusionsThe RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.
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