PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping

Objectives: Phenotype-driven workflows in clinical and translational research require standardized ontology-based representation, ontology-aware cohort discovery, and provenance inspection for each assertion. Existing approaches optimize either for semantic traversal or scalable batch analytics, but not both. We describe PheBee, a hybrid system that links semantic assertions to scalable evidence storage via a deterministic identifier, preserving provenance while supporting ontology-aware discovery at cohort scale. Materials and Methods: PheBee represents phenotype assertions in a knowledge graph as ontology-linked nodes with clinical modifier context (e.g., negated, family history), and stores supporting evidence records in a scalable row-oriented evidence table for cohort-scale access. The two layers are connected by a deterministic identifier enabling stable joins across repeated ingestions without duplicating high-volume evidence in the graph. We evaluated PheBee using synthetic datasets designed to exercise end-to-end ingestion and query workflows. Results: Functional evaluation validated hierarchical term expansion, qualifier-aware retrieval, duplicate-free assertion handling under re-ingestion, and privacy-conscious management of subjects shared across multiple research projects. At scale (10,000 subjects producing 12M evidence records) PheBee completed ingestion in ∼30 minutes and responded to interactive queries within 6 seconds under concurrent load. Discussion: PheBee exposes a unified API for ontology-aware cohort discovery with hierarchical term expansion, subject-centric retrieval of phenotypes and clinical modifiers, and evidence and provenance queries. Its data model aligns with GA4GH Phenopackets, facilitating interoperability with phenotype exchange standards. Conclusion: By combining ontology-aware semantics with scalable, provenance-bearing evidence storage, PheBee provides a practical open-source foundation for phenotype-driven research workflows that demand both semantic precision and cohort-scale traceability. LAY SUMMARY: Researchers often use "phenotypes" (observable clinical features) to describe individual subjects and find groups of similar subjects. Those phenotypes come from many sources and need both standard terminology and clear evidence for why a phenotype has been associated with a subject.PheBee is a software system that stores phenotype assertions in a way that supports both "ontology-aware" searching (for example, finding patients with any subtype of a condition) and scalable storage of supporting evidence across large research cohorts. PheBee uses multiple types of data storage so researchers can perform interactive phenotype searches and also store millions of pieces of supporting evidence. A shared identifier connects the two storage layers, so subjects' phenotypes and their supporting evidence remain linked even as new data is added over time. We evaluated PheBee using fully synthetic (non-patient) data to confirm correct query behavior, evidence traceability, and system performance at large scale.