ArticlePLoS computational biology2017
Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.BMC bioinformatics · 2026Article
- Development and validation of embedded multilayer attention graph convolution neural network models for predicting gut microbe-disease associations.Frontiers in microbiology · 2026Article
- A microbe-drug association prediction model based on graph attention networks and rotation forest.Frontiers in bioinformatics · 2026Article
- BRMDA: prediction model for potential microbe-drug associations based on bilinear attention networks and random forest.Frontiers in genetics · 2026Article
- Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associations.Scientific reports · 2025Article
- HAFMMDA: HIN2vec-Based Attentional Factorization Machines for Predicting Microbe-Drug Associations.Interdisciplinary sciences, computational life sciences · 2025Article
- Computational prediction and conformation of relationships among microbes, drugs and diseases.Scientific reports · 2025Article
- Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes.BMC bioinformatics · 2025Article
- BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs.Briefings in bioinformatics · 2025Article
- BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders.Frontiers in genetics · 2025Article
- Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction.Briefings in bioinformatics · 2024Article
- Predicting potential microbe-disease associations based on dual branch graph convolutional network.Journal of cellular and molecular medicine · 2024Article
- A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest.BMC bioinformatics · 2024Article
- NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network.Scientific reports · 2024Article
- A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders.Frontiers in microbiology · 2024Article
- OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks.Frontiers in genetics · 2024Article
- LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble.Frontiers in microbiology · 2024Article
- BANNMDA: a computational model for predicting potential microbe-drug associations based on bilinear attention networks and nuclear norm minimization.Frontiers in microbiology · 2024Article
- MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs.Frontiers in pharmacology · 2024Article
- Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs.Briefings in bioinformatics · 2023Article
Corrections and comments
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1 author.
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Abstract
The genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise measures of microbe-microbe interaction. The first (genome content similarity index) quantifies similarity in genome composition between two microbes, while the second (microbe-microbe functional association index) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets, accounting for phylogenetic relatedness of the taxa. Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data, but genome composition was found to be a strong predictor as well, it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination, even after accounting for evolutionary relationships between the species. On their own, the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons. In summary, results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology, and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions.
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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.