SYSTEM AND METHOD FOR FEDERATED MULTI-MODAL TRANSLATIONAL OMICS, CLINICAL EHR, IMAGE, AND KNOWLEDGE GRAPH GOVERNANCE AND PIPELINE ORCHESTRATION

# DESCRIPTION AND KEY ARCHITECTURAL INNOVATIONS ## 1. DESCRIPTION OF THE INVENTIONThe disclosed invention relates generally to computational biology, precision oncology, translational medicine, and AI-driven disease model orchestration. More particularly, it provides a disease-agnostic system, architecture, and method for federated multi-modal integration—combining translational multi-omics, electronic health records (EHR), medical and spatial imaging, and white-box gene-regulatory knowledge graphs—governed by zero-raw-data federation (ZRDF) architectures and dynamic self-evolving pipeline orchestration. --- ## 2. KEY ARCHITECTURAL INNOVATIONS * **White-Box Knowledge Graph Governance & LLM Orchestration:** * Replaces black-box inference with mechanistic graph structures (`(:Gene)-[:REGULATES]->(:Pathway)`) interfaced with foundation models. * Governs multi-layer regulatory mechanisms (transcripts, alternative splicing, epigenomics, single-cell/spatial topography) using human-auditable standard operating procedures (SOPs). * **Zero-Raw-Data Federation (ZRDF) Engine:** * Implements a decentralized multi-modal federation framework to synchronize clinical EHR, spatial/medical imaging, and multi-omics across distributed institutions. * Ensures zero raw data exposure by relying exclusively on encrypted embeddings and privacy-preserving model updates. * **Multi-Layer Regulatory Mechanism Modeling & Co-Training:** * Captures intricate regulatory hierarchies spanning genomic variants, chromatin accessibility (ATAC-seq), differential expression, and alternative splicing isoforms. * Enables simultaneous training and evaluation of foundation models alongside transparent, inspectable graph ontologies. * **Closed-Loop Self-Evolving Orchestration:** * Executes continuous train-test-evaluate orchestration that dynamically refines pipeline hypotheses and knowledge base assertions. * Ingests downstream empirical validation data (such as in vitro PROTAC degradation assays, high-throughput screening, and functional cellular assays) to iteratively re-calibrate edge weights and improve predictive accuracy.