ArticlemSystems
Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks.
Article in mSystems. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Artificial Intelligence for Microbial Isolation and Cultivation: Progress and Challenges.Microorganisms · 2026Review
- Knowledge graph embedding for predicting and analyzing microbial interactions.Scientific reports · 2025Article
- Advances in synthetic microbial ecosystems approach for studying ecological interactions and their influencing factors.Engineering microbiology · 2025Review
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- Structure and composition of early biofilms formed on dental implants are complex, diverse, subject-specific and dynamic.NPJ biofilms and microbiomes · 2024Article
- Mixed and membrane-separated culturing of synthetic cyanobacteria-yeast consortia reveals metabolic cross-talk mimicking natural cyanolichens.Scientific reports · 2024Article
- Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning.Communications biology · 2024Article
- Gradient boosted regression as a tool to reveal key drivers of temporal dynamics in a synthetic yeast community.FEMS microbiology ecology · 2024Article
- Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.Microbiology spectrum · 2024Article
- Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations.Communications biology · 2024Article
- Inferring microbial interactions with their environment from genomic and metagenomic data.PLoS computational biology · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities.Quantitative biology (Beijing, China) · 2023Review
- Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.Microbiology spectrum · 2022Article
- Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022Review
- A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).Nature protocols · 2021Article
- Machine learning and applications in microbiology.FEMS microbiology reviews · 2021Review
- Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models.Current opinion in microbiology · 2021Review
- Optimal transportation theory for species interaction networks.Ecology and evolution · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large
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Registered trials
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.