C-S.B.47: MicroKnow: Quantifying the Mechanistic Evidence Gap in Clinical Microbe-Disease Associations
Clinical microbiome databases contain thousands of reported microbe–disease associations, but molecular evidence that could help interpret them is dispersed across resources with incompatible identifiers and relation types. This makes it difficult to determine systematically which reported associations can be connected to curated molecular evidence.
MicroKnow integrates clinical associations from three databases and molecular evidence from five sources in a provenance-preserving Neo4j knowledge graph. We tested 23,006 unique clinical associations involving taxa reported at genus level or below for either of two disease-matched routes: microbe–metabolite–human gene or molecular target–disease, or microbe–metabolite–disease. Both routes had to terminate at the disease reported in the clinical association. At least one route was retrieved for 1,875 associations (8.15%), leaving 21,131 (91.85%) in the molecular-evidence gap. The gap remained above 90% across taxonomic analysis sets and increased to 94.05% after excluding 69 high-degree metabolites. Thus, most reported associations were not connected by the predefined routes in the integrated databases, while part of the retrieved coverage depended on broadly connected molecular intermediates. Failure to retrieve a route does not establish that no biological mechanism exists. Applying the same route definitions in reverse, followed by disease-eligibility and hub controls, retained 2,577 graph-derived follow-up candidates absent from the three integrated clinical databases. These candidates are ordinal priorities for literature review and experimental follow-up, not predicted probabilities. MicroKnow provides traceable evidence routes and a reproducible framework for identifying where curated molecular evidence is available and where important gaps remain.Contact Attendee
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