We present a modular, four-layer biological knowledge graph (BKG) framework for predicting clinical phenotypes from multiomic data without requiring labeled training data, supporting precision medicine applications across novel pathogens, drug safety, and rare disease research. The architecture separates curated biological knowledge from population-level experimental observations, individual subject measurements, and computationally inferred relationships, enabling flexible data integration without graph reconstruction. The computationally inferred relationship layer is populated through a deterministic reasoning engine designed to produce outputs that are both interpretable and independently verifiable. We train a path-based graph neural network on the Neo4j BKG, validated through perturbation controls, to turn raw model output into trustworthy, decision-grade reasoning. Its traceable, multi-hop gradient paths form a deterministic backbone that steers the stochastic LLM agents layered above it. A multi-phase verification machine then reads those paths and independently scores each hypothesis across multiple public databases, with no LLM in the validation loop. By keeping reasoning and validation epistemically independent, the framework delivers a GNN backbone that is reproducible, auditable, and resistant to the hallucination failures of LLM-only systems. We present this as an open framework that extends into an agentic, self-improving loop—swarms of reader and verifier agents governed by a shared adversarial critique layer and anchored by the deterministic GNN prior—delivering a scalable, cost-efficient, and governable path to production.
Speaker
Evan Harris Peikon
Graduate Student, George Mason University
Evan Peikon is PhD student at George Mason University, where he researches multiomics approaches to identifying predictive, prognostic, and diagnostic network biomarkers in cancer and infectious disease. Prior to that, he co-founded NNOXX, where he co-developed and led the clinical validation of novel digital and molecular biosensors, taking a first-to-market technology from concept to commercialization. He also serves as an advisor to several health and biotechnology companies.
