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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
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PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping.

David M Gordon, Max Homilius, Austin A Antoniou

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    PubMed
    Summary
    This summary is machine-generated.

    PheBee is a novel system for phenotype-driven research, linking semantic assertions to scalable evidence storage. It enables ontology-aware cohort discovery and preserves provenance for traceability in large-scale translational studies.

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    Area of Science:

    • Biomedical Informatics
    • Translational Research
    • Computational Biology

    Background:

    • Phenotype-driven research requires standardized data representation and ontology-aware cohort discovery.
    • Existing systems often compromise between semantic traversal and scalable analytics.
    • Traceability of phenotype assertions and supporting evidence is crucial for clinical research.

    Purpose of the Study:

    • To introduce PheBee, a hybrid system designed to integrate semantic phenotype assertions with scalable evidence storage.
    • To enable both ontology-aware cohort discovery and provenance inspection for research workflows.
    • To provide a practical foundation for phenotype-driven research demanding semantic precision and cohort-scale traceability.

    Main Methods:

    • PheBee employs a dual-layer architecture: a knowledge graph for ontology-linked phenotype assertions and a scalable evidence table for supporting records.
    • A deterministic identifier links these layers, ensuring stable joins and preventing evidence duplication.
    • The system was evaluated using synthetic datasets to test ingestion, querying, and provenance capabilities.

    Main Results:

    • PheBee successfully validated hierarchical term expansion, qualifier-aware retrieval, and duplicate-free assertion handling.
    • The system demonstrated efficient performance at scale, with ingestion completing in approximately 30 minutes and interactive queries responding within 6 seconds under concurrent load for 10,000 subjects and 12 million evidence records.
    • Functional evaluation confirmed privacy-conscious management of shared subjects across research projects.

    Conclusions:

    • PheBee offers a unified API for ontology-aware cohort discovery, phenotype retrieval, and evidence/provenance queries.
    • Its data model aligns with GA4GH Phenopackets, promoting interoperability with phenotype exchange standards.
    • PheBee provides an open-source solution combining semantic precision with scalable, provenance-bearing evidence storage for phenotype-driven research.