ArticleBMC genomics2020
Inferring directional relationships in microbial communities using signed Bayesian networks.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bayesian networks for network inference in biology.Journal of the Royal Society, Interface · 2025Review
- Beyond Microbial Variability: Disclosing the Functional Redundancy of the Core Gut Microbiota of Farmed Gilthead Sea Bream from a Bayesian Network Perspective.Microorganisms · 2025Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.mSystems · 2024Article
- Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.Ecology and evolution · 2024Article
- Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.bioRxiv : the preprint server for biology · 2023Article
- Article
- A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis.Microbial genomics · 2022Article
- Agent-Based Modeling of Microbial Communities.ACS synthetic biology · 2022Review
- Review
- Causal effects in microbiomes using interventional calculus.Scientific reports · 2021Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicrobe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations.
resultsIn this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders.
conclusionsBNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.
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Registered trials
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