Dietary Flavonoid Effects Predicted via Multi-Evidence Network Pharmacology Framework

A master network of 278,768 interactions connects 17,869 human proteins, 14 dietary flavonoids, and 1,496 FDA-approved drugs. The framework, developed by Koyo Fujisaki and last updated in April 2026, quantitatively predicts therapeutic properties, with computational associations explaining 84% of variance in experimental potency. Predictions are translated to 506 foods, yielding 685 food-therapeutic combinations.

Use Cases

• Predicting multi-target therapeutic effects of dietary flavonoids based on protein interaction networks.
• Validating computational predictions of flavonoid bioactivity against experimental cytotoxicity data.
• Identifying literature-supported food-therapeutic associations for cardiovascular and anticancer effects.
• Ranking food categories like tomato, cranberry, and tea by strength of evidence for specific therapeutic properties.

Strengths

• Quantitative framework validated experimentally, with computational association strength explaining 84% of variance in potency (Pearson r = 0.918).
• Master network integrates 278,768 interactions among 17,869 proteins, 14 flavonoids, and 1,496 drugs.
• Predictions are supported by multiple evidence tiers: computational, experimental (cell models), epidemiological, and systematic literature analysis (132 unique references).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Dataset is small (75.8 KB), indicating limited scope.

Provenance

Source

Koyo Fujisaki via figshare.

Collection Method

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

Freshness

Last updated 2026-04-14 04:14:40; freshness should be verified.