Salivary Microbiome Meta-Analysis for Multi-Disease Prediction

A meta-analysis of 22 cohorts comprising 7,750 saliva samples collected from PubMed between 2016 and 2024. Bioinformatics analyses using QIIME2 and Wekemo revealed core microbiota and constructed multi-disease prediction models. The dataset was authored by Qixiang Yuan and published on figshare under a CC-BY-4.0 license.

Use Cases

• Training multi-class random forest models for disease classification based on microbial profiles.
• Identifying core salivary microbiota associated with healthy baselines.
• Analyzing gender differences in oral microbiota composition.
• Investigating associations between microbiota structure and physiological/pathological states.
• Validating the feasibility of saliva microbiota for non-invasive disease diagnosis.

Strengths

• Includes 7,750 samples from 22 cohorts.
• Models demonstrated robust classification performance (AUC: 0.898–1).
• Identified nine core microbiota genera in the negative control group.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect temporal/source bias inherent to the PubMed search (2016–2024).

Provenance

Source

figshare

Collection Method

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

Time Range

2016–2024

Freshness

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