Data Sheet 3_Salivary microbial meta-analysis reveals gender differences in oral microbiot

A meta-analysis of public 16S saliva data by Qixiang Yuan, published on figshare in April 2026, integrates 7,750 samples from 22 cohorts sourced from PubMed between 2016 and 2024. Bioinformatics analyses using QIIME2 and Wekemo revealed community characteristics, identified nine core microbes in healthy controls, and built multi-disease prediction models. The study validated the feasibility of establishing healthy baselines via saliva microbiota and using machine learning for non-invasive diagnosis.

Use Cases

• Train multi-class disease prediction models based on salivary microbial features.
• Identify gender differences in oral microbiota composition.
• Establish a baseline of core healthy salivary microbes for comparative studies.
• Analyze associations between microbiota structure and physiological/pathological states.
• Validate the use of 16S rRNA data for non-invasive diagnostic applications.

Strengths

• Integrates 7,750 samples from 22 cohorts, providing a substantial sample pool.
• Models achieved robust classification performance with AUCs ranging from 0.898 to 1.
• Identified nine specific core microbiota genera in the negative control group.
• Meta-analysis spans data from PubMed published between 2016 and 2024.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is a PDF file (17.5 MB), likely containing summary results rather than raw sequence data.

Provenance

Source

figshare

Collection Method

Meta-analysis of public 16S saliva data from PubMed, processed with QIIME2 and Wekemo.

Time Range

2016–2024

Freshness

Last updated 2026-04-15 05:46:09; freshness should be verified.