ArticlePLoS computational biology2021
MiMeNet: Exploring microbiome-metabolome relationships using neural networks.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- The gut microbiome-metabolome dataset collection: a curated resource for integrative meta-analysis.NPJ biofilms and microbiomes · 2022Pooled it
- Altitude-Associated Divergence of the Gut Microbiome in Endangered Forest Musk Deer: Evidence From Integrated Metagenomics, Metabolomics, and Culturomics.Evolutionary applications · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Basic Microbiome Analysis: Analytical Steps from Sampling to Sequencing.Microorganisms · 2026Review
- Primate gut microbiota induce evolutionarily salient changes in mouse neurodevelopment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies.PloS one · 2026Article
- A guide to network analysis, multi-omics integration, and applications in livestock microbiome research.World journal of microbiology & biotechnology · 2025Review
- Review
- Unravelling three-way interactions betweenJournal of medical microbiology · 2025Review
- A systematic benchmark of integrative strategies for microbiome-metabolome data.Communications biology · 2025Article
- VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data.Briefings in bioinformatics · 2025Article
- DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts.BMC bioinformatics · 2025Article
- Fungal Metabolomics: A Comprehensive Approach to Understanding Pathogenesis in Humans and Identifying Potential Therapeutics.Journal of fungi (Basel, Switzerland) · 2025Review
- Predicting metabolite response to dietary intervention using deep learning.Nature communications · 2025Article
- Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with Next-Generation Sequencing data.Journal of microbiology (Seoul, Korea) · 2025Review
- Integrative AI-Based Approaches to Connect the Multiome to Use Microbiome-Metabolome Interactive Outcome as Precision Medicine.Methods in molecular biology (Clifton, N.J.) · 2025Article
- PredCMB: predicting changes in microbial metabolites based on the gene-metabolite network analysis of shotgun metagenome data.Bioinformatics (Oxford, England) · 2024Article
- The primate gut microbiota contributes to interspecific differences in host metabolism.Microbial genomics · 2024Article
- Predicting metabolite response to dietary intervention using deep learning.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through "Guilt by Association". Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.
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What Socratic holds
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 Socratic graph.