Salivary Microbiota Meta-Analysis for Multi-Disease Prediction

A meta-analysis of 7,750 saliva samples from 22 cohorts published between 2016 and 2024. The dataset was compiled by Qixiang Yuan from PubMed and analyzed using bioinformatics tools like QIIME2 and Wekemo. It reveals gender differences, core microbiota, and molecular markers for disease prediction.

Use Cases

• Building multi-class disease prediction models based on salivary microbial features.
• Identifying core microbiota signatures for establishing healthy baselines.
• Analyzing gender differences in oral microbiota composition.
• Investigating associations between specific genera (e.g., Streptococcus, Haemophilus) and disease states.

Strengths

• Contains 7,750 samples aggregated from 22 cohorts.
• Meta-analysis spans studies from 2016 to 2024.
• Reports robust model performance (AUC: 0.898–1).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect temporal and source bias inherent to the PubMed search.

Provenance

Source

figshare

Collection Method

Meta-analysis of public 16S saliva data from PubMed.

Time Range

2016–2024

Freshness

Last updated 2026-04-15 05:46:08; freshness should be verified.