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Methodology · Explainable pipeline

How HerbaGraph turns labs into evidence-graded recommendations

Seven deterministic stages connect biomarkers to biological systems, pathways, a knowledge graph, and literature-backed intervention classes — with dual knowledge paths you can A/B test.

7pipeline stages
9recommendation trees
28biological pathways
2knowledge paths

The 7-stage analysis pipeline

Click a stage to see what happens, what data moves, and where recommendations come from.

    Two knowledge paths — same labs, different substrate

    Legacy catalogs

    Production default. Curated intervention catalogs, Tier A evidence claims with PMIDs, and live literature retrieval (PubMed / ClinicalTrials / Europe PMC).

    • Routable claims on 28 priority pathways
    • Nine test-type recommendation trees
    • Deterministic catalog fallback when LLM is unstable
    Recommended for clinical review

    Canonical graph engine

    Experimental A/B path. Pathway activations traverse MODULATES / TARGETS / ACTIVATES / INHIBITS edges on canonical_entities, ranked by evidence type and confidence.

    • PMID-backed edges from evidence claims
    • Predicted edges from catalog mechanisms (~700 long-tail interventions)
    • CONTAINS composition for food sources
    Graph-backed · hybrid fill

    Knowledge graph anatomy

    Recommendations are not a black box. They are walks on a typed graph with provenance.

    Biomarker CRP · Iron · TSH Lab status high / low / genotype Pathway NF-κB · IRON · HPA MODULATES edge + confidence TARGETS biomarker / target Intervention herb · nutrient · food CONTAINS food → compound

    Recommendation trees & pathway density

    Nine recommendation trees
    Example pathway claim density (illustrative)

    Charts are schematic for education. Live counts evolve as catalogs and graph edges expand.

    From graph hits to the clinical decision map

    1. Safety layer — contraindications, drug interactions, regulated flags strip or demote items.
    2. Evidence confidence — study type, quality score, catalog connectivity, and uncertainty.
    3. Four lanes — direct biomarker, lifestyle, supportive adjunct, regulated context.
    4. Explainability — supporting literature, pathway drivers, and limitations stay attached to each recommendation.
    1Direct biomarker
    2Lifestyle
    3Supportive adjunct
    4Regulated context

    What HerbaGraph does not do

    Not a diagnosis

    Outputs are evidence synthesis and biological reasoning for research and education. They do not replace licensed medical judgment.

    Not invented literature

    Tier A claims carry PMIDs. Predicted graph edges are labeled PREDICTED and scored lower than clinical or meta-analytic edges.

    See it on a real-shaped report

    Walk a sample clinical report, or upload your own labs in the workspace.