Salivary Microbiome Meta-Analysis for Multi-Disease Prediction, 7,750 Samples

A meta-analysis of 22 public 16S rRNA saliva cohorts comprising 7,750 samples, collected from PubMed between 2016 and 2024. The study by Qixiang Yuan identifies core microbiota and constructs machine learning models for multi-disease prediction. It was last updated on 2026-04-15 and is shared under a CC-BY-4.0 license.

Use Cases

• Training multi-class random forest models for disease classification based on salivary microbiota profiles.
• Identifying core microbial genera, such as Streptococcus and Haemophilus, associated with healthy baselines.
• Analyzing differences in microbiota structure across physiological and pathological states mentioned in the study.
• Validating the feasibility of using saliva as a non-invasive diagnostic medium for various diseases.

Strengths

• Includes 7,750 samples aggregated from 22 cohorts, providing a substantial sample size for meta-analysis.
• Reports robust model performance with AUC scores ranging from 0.898 to 1.000 for multi-disease classification.
• Identifies nine core microbiota genera in the negative control group, establishing a reference baseline.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying feature table is unknown, which may limit suitability assessment.
• The dataset is very small (5.8 KB), suggesting it contains summary or processed results rather than raw sequence data.

Provenance

Source

figshare, author Qixiang Yuan.

Collection Method

Meta-analysis of public 16S rRNA sequence data from PubMed (2016–2024), processed with QIIME2 and Wekemo.

Time Range

Studies published between 2016 and 2024.

Freshness

Last updated 2026-04-15 05:46:06; freshness should be verified.