Dietary Flavonoid Network Pharmacology Predictions with Multi-Evidence Framework

A master network of 17,869 human proteins, 14 dietary flavonoids, and 1,496 FDA-approved drugs (278,768 interactions) was constructed to predict therapeutic properties. The framework integrates computational predictions with experimental and epidemiological evidence, translating predictions to 506 foods and 685 food-ATC therapeutic combinations. The dataset was authored by Koyo Fujisaki and last updated on 2026-04-14.

Use Cases

• Predicting multi-target therapeutic effects of dietary flavonoids based on protein interaction networks.
• Validating computational predictions of flavonoid bioactivity using experimental cytotoxicity data.
• Identifying literature-supported food-therapeutic associations based on systematic PubMed analysis.
• Analyzing target protein overlap between flavonoids and FDA-approved drugs for cardiovascular or anticancer effects.

Strengths

• Quantitative network includes 278,768 interactions, providing a basis for statistical analysis.
• Experimental validation in cancer cell models showed high predictive accuracy (R² = 0.843).
• Framework integrates computational, experimental, and epidemiological evidence for 96 literature-supported associations.

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 small (26.4 KB), indicating limited scope or summary-level data.

Provenance

Source

figshare

Collection Method

Constructed via a multi-tiered network pharmacology framework integrating computational prediction, experimental validation, and epidemiological evidence analysis.

Freshness

Last updated 2026-04-14 04:14:43; freshness should be verified.