ArticleGut microbes
Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases.
Article in Gut microbes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Gut microbial community of patients with Parkinson's disease analyzed using metagenome-assembled genomes.Neural regeneration research · 2026Article
- A Comparison of the Gut Microbiome of Two Sympatric Macropods Along an Urbanisation Gradient in Tasmania.Environmental microbiology reports · 2026Article
- Insights into pig resilience: the Microbiome-genetic connection.Porcine health management · 2026Review
- Preliminary insights into gut microbiome shifts as screening proxy for MASLD disease progression.Scientific reports · 2026Article
- The role of nutrition and multimodal lifestyle interventions in Alzheimer's prevention and management: a mini-review.Frontiers in nutrition · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Identification of urinary bacterial genes as biomarkers for non-invasive diagnosis of renal lupus.Biomarker research · 2025Article
- Exploring Gut Microbiota in Systemic Lupus Erythematosus: Insights and Biomarker Discovery Potential.Clinical reviews in allergy & immunology · 2025Review
- Gut microbiota: a promising new target in immune tolerance.Frontiers in immunology · 2025Review
- From the Gut to the Brain: Is Microbiota a New Paradigm in Parkinson's Disease Treatment?Cells · 2024Review
- Celiac disease gut microbiome studies in the third millennium: reviewing the findings and gaps of available literature.Frontiers in medical technology · 2024Review
- The causal relationship between the human gut microbiota and pyogenic arthritis: a Mendelian randomization study.Frontiers in cellular and infection microbiology · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions.
designWe assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction.
resultsWe evaluated 52 bacterial taxa and pre-selected PV (
conclusionOur findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities.
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