ArticleFrontiers in microbiology2024
Machine learning prediction of obesity-associated gut microbiota: identifying
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deciphering Post-surgery Gut Microbial Dynamics in Colorectal Cancer through Multi-cohort Machine Learning.Current microbiology · 2026Article
- Review
- TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata.BMC bioinformatics · 2025Article
- Integrating Machine Learning to Identify Key Microbiota of Gut Community Changes Across Different Stages in Dahe Black Pigs.Microorganisms · 2025Article
- Microbiota-Host Interactions: Exploring Their Dynamics and Contributions to Human Diseases.MicrobiologyOpen · 2025Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The rising prevalence of obesity and related metabolic disorders highlights the urgent need for innovative research approaches. Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could significantly enhance obesity management strategies. Methods: We leveraged gut microbiome data from 1,563 healthy individuals and 2,043 overweight patients sourced from the GMrepo database. We assessed the anti-obesity effects of Results: Our analysis revealed a significant correlation between gut bacterial composition and body weight. The top 40 bacterial species were utilized to develop ML models, with XGBoost demonstrating the highest predictive accuracy. SHAP analysis indicated a negative association between the relative abundance of six bacterial species, including Conclusion:
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