This project is part of the Virtual Human Platform for Safety (VHP4S), which involves integrating medical data, creating graph-based knowledge management systems, and using advanced tools like Retrieval Augmented Generation (RAG) for disease-specific information retrieval. The primary focus is on three case studies: Parkinson's disease, kidney disease, and thyroid disorders.
This project makes use of two primary Python libraries:
- biochatter==0.7.5: A tool for generating biomedical-related text and integrating natural language generation with biomedical ontologies.
- biocypher==0.5.44: A library for creating and managing biomedical graphs in Neo4j and integrating data from multiple sources for building knowledge networks.
To use this project, ensure you have the following installed:
- Python 3.8+
- Neo4j (if using graph database functionalities)
- pip (Python package manager)