ArticleMicrobiome2019
Dynamic interaction network inference from longitudinal microbiome data.
Article in Microbiome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- A temporal-aware machine learning framework enables microbial community dynamics prediction with personalized precision.Microbiome · 2025Article
- Inference of causal interaction networks of gut microbiota using transfer entropy.BMC genomics · 2025Article
- A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.BMC genomics · 2025Article
- Bayesian networks for network inference in biology.Journal of the Royal Society, Interface · 2025Review
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.mSystems · 2024Article
- Article
- Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.eLife · 2024Review
- A digital twin of the infant microbiome to predict neurodevelopmental deficits.Science advances · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- A manifold-based framework for studying the dynamics of the vaginal microbiome.NPJ biofilms and microbiomes · 2023Article
- Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.bioRxiv : the preprint server for biology · 2023Article
- Prevalence and influencing factors of hyperuricemia in middle-aged and older adults in the Yao minority area of China: a cross-sectional study.Scientific reports · 2023Article
- Deciphering associations between gut microbiota and clinical factors using microbial modules.Bioinformatics (Oxford, England) · 2023Article
- Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Ecology and evolution · 2023Article
- Benchmarking differential abundance analysis methods for correlated microbiome sequencing data.Briefings in bioinformatics · 2023Article
- Capturing the dynamics of microbial interactions through individual-specific networks.Frontiers in microbiology · 2023Article
- Microbiome maps: Hilbert curve visualizations of metagenomic profiles.Frontiers in bioinformatics · 2023Article
- 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
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4 authors.
Funding
Abstract
backgroundSeveral studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.
resultsHere, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions. The extended CGBayesNets package is freely available under the MIT Open Source license agreement. The source code and documentation can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public .
conclusionsWe propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.