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Context-Aware Multi-Agent AI System to Explore the Oxidative Stress in Cardiovascular Medicine

Overview

This repository contains the implementation of a context-aware multi-agent AI system designed to explore the complex interplay between oxidative stress (OS) and cardiovascular diseases (CVDs). The system leverages advanced AI methodologies, including knowledge graph (KG) construction, graph neural networks (GNNs) for link prediction, and a modular multi-agent framework to dynamically validate and refine insights. The objective is to bridge the gap between fragmented biomedical data and actionable discoveries, accelerating research in cardiovascular medicine.

Features

  • Comprehensive Knowledge Graph (KG):

    • Integrates biomedical data from PubMed, UniProt, DrugBank, and Reactome.
    • Models nodes (proteins, pathways, drugs, diseases) and edges (relationships) with high fidelity.

  • Graph Neural Network (GNN):

    • Implements state-of-the-art GNN models for predicting novel OS-CVD relationships.
    • Identifies high-confidence links between biomarkers, pathways, and drug targets.

  • Multi-Agent AI Framework:

    • Modular architecture with specialized agents:
      • UniProt Agent: Protein data and functional annotations.
      • CVD Agent: Pathways and mechanisms underlying cardiovascular diseases.
      • OS Agent: Analysis of oxidative stress biomarkers and mechanisms.
      • Drug Agent: Drug interactions and therapeutic implications.
      • Reactome Agent: Systems-level analysis of metabolic and signaling pathways.
    • Central orchestrator for task management and inter-agent communication.

  • Dynamic Analysis and Refinement:

    • Agents leverage contextual understanding to refine predictions.
    • Feedback loops ensure iterative improvement of outputs.

  • Interactive Insights Visualization:

    • Visualizes KGs and predicted relationships via interactive dashboards.