ReviewFrontiers in genetics2019
A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 3 of them syntheses that pooled it.
What it found
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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
91 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice.Cell reports · 2023Pooled it
- Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.PLoS computational biology · 2022Pooled it
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- Biotechnological Approaches to Probiotics and Postbiotics through the Gut-Brain Axis Modulation.Iranian biomedical journal · 2026Article
- EXPLANA: a user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies.Bioinformatics (Oxford, England) · 2026Article
- Plant-soil-microbiome interactions: mechanisms, advances, and challenges in sustainable agriculture and healthy agroecosystems.Frontiers in microbiology · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Hybrid expert system for robust detection of rare sequence signals: a computational proof-of-concept in host-dominated backgrounds.Frontiers in microbiology · 2026Article
- Decoding host-microbiome interactions through integrative multi-omics and artificial intelligence: from biological mechanisms to predictive modelling.Frontiers in microbiomes · 2026Review
- MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data.Microbiome · 2025Article
- Deciphering the personalized functional redundancy hierarchy in the gut microbiome.Microbiome · 2025Article
- Integrating Microbiomes for Regenerative Food Systems: Recent Insights, Implementations, and Emerging Trends.Food science & nutrition · 2025Review
- Effects of zacopride and multidimensional impacts of cross-kingdom symbiosis: gut microbiota modulates coronary microvascular dysfunction via the chlorophyll/heme-tryptophan metabolic axis.Journal of translational medicine · 2025Article
- Effects of data transformation and model selection on feature importance in microbiome classification data.Microbiome · 2025Article
- Editorial: Harnessing machine learning to decode plant-microbiome dynamics for sustainable agriculture.Frontiers in microbiomes · 2025Article
- Article
- UniCor and UniCorP: a novel metric and hierarchical feature selection algorithm for microbial community analysis.ISME communications · 2025Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.
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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.