DiscoADRD

DiscoADRD (Discovery Agent for Alzheimer's Disease and Related Dementias) is an AI-powered multi-agent scientific discovery system designed for analyzing NACC (National Alzheimer's Coordinating Center) datasets and generating research insights through automated hypothesis testing and statistical analysis.

🎯 Overview

DiscoADRD employs a sophisticated multi-agent architecture where specialized AI agents (Scientist, Critic, and Planning agents) collaborate to:

• Analyze complex medical datasets
• Generate and test scientific hypotheses
• Perform comprehensive statistical analysis
• Generate research insights and findings
• Automate the scientific discovery workflow

📁 Repository Structure

DiscoADRD/
├── DiscoADRD_v3/              # Latest version (recommended)
│   ├── discover.py            # Main entry point
│   ├── config.yml             # Configuration file
│   ├── run.sh                 # HPC job submission script
│   ├── batch.csv              # Sample batch hypotheses file
│   └── discovery_agent_pkg/   # Core package
│       ├── agents/            # AI agent implementations
│       ├── core/              # Core system components
│       ├── data/              # Data processing modules
│       ├── execution/         # Code execution system
│       ├── experiment/        # Experiment management
│       └── ui/                # User interface components
│
├── vLLM/                      # vLLM inference client utilities
│   ├── client.py              # vLLM multi-GPU client
│   ├── discovery_agent_client.py
│   └── run.sh                 # vLLM runner script
│
├── LICENSE                    # License file
└── README.md                  # This file

🚀 Quick Start

Prerequisites

• Python 3.8+
• CUDA-compatible GPU (recommended)
• Access to NACC datasets
• HPC environment with SGE/PBS support (optional)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd DiscoADRD

2. Set up environment (for HPC environments):

   module load miniconda
   module load cuda
   conda activate llms

3. Install dependencies:

   pip install transformers torch huggingface-hub python-dotenv
   pip install pandas numpy scipy scikit-learn matplotlib seaborn
   pip install pyyaml tqdm vllm

4. Configure the system:

   • Edit DiscoADRD_v3/config.yml with your dataset paths and settings
   • Update paths in run.sh scripts to match your environment

Basic Usage

cd DiscoADRD_v3
python discover.py

For detailed usage instructions, see DiscoADRD_v3/README.md.

📚 Documentation

• DiscoADRD_v3/README.md - Comprehensive documentation for the latest version
• discovery_agent_pkg/README.md - Package-level documentation

🔧 Key Features

• Multi-Agent Architecture: Scientist, Critic, and Planning agents work collaboratively
• Advanced Statistical Analysis: Comprehensive statistical testing including regression, survival analysis, and ML
• NACC Dataset Integration: Specialized for Alzheimer's disease research
• Automated Code Execution: Safe Python code execution with timeout protection
• Multiple Hypothesis Modes: Direct, batch, and keyword-based hypothesis generation
• HPC Integration: Designed for high-performance computing environments

🏗️ Architecture

The system uses a modular architecture with clear separation of concerns:

• Agents: Specialized AI agents for different roles (Scientist, Critic, Planner)
• Core: Main orchestration and configuration management
• Data: Data processing, caching, and file management
• Execution: Safe code execution with sandboxing
• Experiment: Experiment lifecycle management
• UI: User interface and progress tracking

📊 Supported Workflows

1. Direct Hypothesis Testing: Test a specific hypothesis directly
2. CSV Batch Processing: Process multiple hypotheses from a CSV file
3. Keyword-Based Generation: Generate hypotheses from keyword sampling
4. Direct Mode: Process without hypothesis generation

🔬 Research Applications

DiscoADRD is specifically designed for:

• Alzheimer's Disease and Related Dementias (ADRD) research
• NACC dataset analysis
• Automated hypothesis generation and testing
• Clinical research data analysis
• Epidemiological studies

📝 Configuration

Configuration is managed through:

• YAML files: config.yml for system-wide settings
• Command-line arguments: Override YAML settings
• Environment variables: For HPC and system-specific settings

See DiscoADRD_v3/config.yml for detailed configuration options.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📄 License

See LICENSE for license information.

📞 Support

For questions and support:

• Check the documentation in DiscoADRD_v3/README.md
• Review configuration options in config.yml
• Examine verbose output logs
• Contact the development team

🔄 Version Information

• DiscoADRD_v3: Latest version with enhanced features (recommended)
• vLLM: Utilities for vLLM inference client

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Last updated: January 2025