This project aims to build a comprehensive Knowledge Graph (KG) for rare diseases, leveraging the LightRAG framework and data from Orphanet to aid in understanding, diagnosing, and treating rare diseases. The knowledge graph will include information on rare disease symptoms, genetic mutations, treatment options, and other relevant data. Using LightRAG allows us to efficiently extract, represent, and reason over complex disease-related data with the power of Large Language Models (LLM).
- Build a Knowledge Graph to organize and represent structured data on rare diseases.
- Integrate LightRAG for flexible and effective data management.
- Use Orphanet data for rare disease information, enhancing the graph with expert-curated knowledge.
- Enable automated reasoning about rare diseases, symptoms, and treatments, to assist clinicians and researchers.
LightRAG is an advanced framework designed for integrating external data sources into a structured, graph-based model. It leverages large language models (LLMs) for automatic extraction and reasoning, making it highly suitable for applications in healthcare and rare disease research.
Orphanet is a comprehensive, global database dedicated to information on rare diseases and orphan drugs. It provides structured data on rare diseases, including disease descriptions, genetic information, and available treatments, making it an invaluable resource for this project.
Run with Gemini and chunk data on rare diseases to extract entities and relationships with full medical meaning.