Critical Points: Gene Ontology + RAG for Biomedical AI

The Problem Space

Traditional RAG Fails in Biomedicine
Standard vector embeddings and keyword search cannot handle the complexity of biomedical terminology—synonyms, protein families, pathways, and inconsistent naming create retrieval chaos that undermines LLM accuracy.

LLMs Hallucinate Biological Facts
Without structured grounding, LLMs fabricate gene functions, protein interactions, and pathways—a dangerous liability in healthcare and pharmaceutical applications where accuracy is non-negotiable.

GO Provides the Missing Structure
Gene Ontology organizes 20,000+ human genes with expert-curated knowledge across molecular functions, biological processes, and cellular components—the world’s largest source of validated gene function information.

Below is an illustration of Gene Ontology

Core Applications

Technical Advantages

Hybrid Retrieval Architecture
Modern systems combine vector similarity, keyword search, and graph traversal in unified pipelines, leveraging the strengths of each approach for comprehensive context extraction.

Token Efficiency Without Accuracy Loss
Knowledge graph integration reduces token consumption by over 50% while maintaining or improving accuracy—a critical advantage for cost-effective large-scale deployments.

Handles Class Imbalance
RAG consistently outperforms zero-shot prompting on low-prevalence ontology terms rarely encountered in training data, solving a fundamental limitation of pure LLM approaches.

Strategic Impact

Explainable, Auditable AI
GO-RAG provides evidence chains tracing from query to source, meeting the transparency requirements for clinical decision support, regulatory submissions, and scientific publication.

Production-Ready at Scale
Multiple frameworks (OntologyRAG, KG-RAG, BioGraphRAG) demonstrate that ontology-enhanced RAG is not experimental—it’s deployed in real-world biomedical applications handling millions of publications with automated knowledge discovery.

Key Frameworks to Explore

• OntologyRAG (IQVIA): https://github.com/iqvianlp/ontologyRAG
• KG-RAG (UC San Francisco): https://github.com/BaranziniLab/KG_RAG
• SPOKE Knowledge Graph: https://spoke.rbvi.ucsf.edu

Gene Ontology Consortium: https://geneontology.org