Advancing Functional Genomic Interpretation through Large Language Model Empowered Agents Navigating Hierarchical Biological Knowledge Graphs and Regulatory Networks
Keywords:
Functional Genomics, Large Language Models, Multi-Agent Systems, Knowledge Graphs, Regulatory Networks, Socio-Technical Infrastructure, Genomic Governance.Abstract
The interpretation of functional genomics remains one of the most significant challenges in modern precision medicine and molecular biology, characterized by an overwhelming volume of multi-omic data and complex regulatory interdependencies. Traditional computational methods often struggle to bridge the gap between statistical significance and biological meaning. This paper proposes a system-level shift toward the deployment of Large Language Model (LLM) empowered agents designed to navigate hierarchical biological knowledge graphs and gene regulatory networks autonomously. By leveraging the reasoning capabilities of generative AI alongside the structured rigidity of biological ontologies, these agents provide a multi-layered interpretive framework that integrates transcriptomic, proteomic, and epigenetic data. The architectural discussion focuses on the trade-offs between agentic autonomy and the deterministic constraints required for clinical validity. We examine the infrastructure necessary to support massive-scale graph traversal and the socio-technical implications of deploying autonomous agents within biological research ecosystems. Key emphasis is placed on system robustness, the sustainability of high-compute genomic pipelines, and the ethical governance of AI-driven interpretation. Furthermore, we address the necessity of fairness in data representation to avoid algorithmic bias in genomic medicine. This research concludes that while LLM-empowered agents offer transformative potential for accelerating discovery, their deployment necessitates a rigorous policy framework and a resilient infrastructure to ensure scientific integrity and biosecurity in an increasingly automated research landscape.
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