MetaCore

Advanced Meta-Analysis & Systematic Review Platform with AI-Powered Intelligence

MetaCore is a modern, full-stack web application designed to streamline meta-analysis research workflows. It combines AI-driven analysis capabilities with intuitive data visualization tools, making complex statistical analysis accessible to researchers and clinicians.

✨ Features

🤖 AI Meta Agent

• Intelligent analysis automation using machine learning
• Structured reasoning with step-by-step data interpretation
• Automatic detection and handling of missing data
• Multi-outcome analysis support

📊 Advanced Analytics

• Multiple Effect Measures: Odds Ratio (OR), Risk Ratio (RR), Mean Difference (MD)
• Statistical Models: Random Effects (RE) and Fixed Effects (FE) models
• Interactive Visualizations: Forest plots, bias detection charts, compliance metrics
• Real-time Statistics: Dynamic calculations with instant feedback

📁 Data Management

• CSV import and export functionality
• Saved analyses with version history
• Structured data editor for manual entry
• File management system with automatic backups

🔒 Secure Authentication

• Email/password authentication with bcrypt encryption
• Google OAuth integration
• JWT-based session management
• User-specific data isolation

🎨 Additional Analysis Views

• Forest Plot Visualization: Study-by-study effect size display
• Bias Assessment: Publication bias and heterogeneity detection
• Compliance Tracking: Regulatory and protocol adherence monitoring
• Advanced Statistics: Extended statistical analysis options

🚀 Tech Stack

Frontend

• React 19 - Modern UI library with hooks
• Vite - Lightning-fast build tool and dev server
• Tailwind CSS 4 - Utility-first styling
• Lucide React - Beautiful icon library
• Context API - State management

Backend

• Node.js + Express 5 - Fast, scalable server
• MongoDB - NoSQL database
• Mongoose - MongoDB ODM
• JWT - Secure authentication
• bcryptjs - Password hashing
• Google Auth Library - OAuth integration
• CORS - Cross-origin request handling

📋 Prerequisites

• Node.js (v16+)
• MongoDB instance (local or cloud, e.g., MongoDB Atlas)
• Google OAuth credentials (optional, for Google login)

🛠️ Installation & Setup

1. Clone the Repository

git clone https://github.com/smshozab/MetaCore.git
cd MetaCore

2. Install Dependencies

npm install

3. Environment Configuration

Create a .env file in the root directory:

# Database
MONGODB_URI=mongodb://localhost:27017/metacore
# or for MongoDB Atlas:
# MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/metacore

# JWT
JWT_SECRET=your_jwt_secret_key_here

# Google OAuth (optional)
GOOGLE_CLIENT_ID=your_google_client_id_here

# Server
PORT=5000

# CORS
ALLOWED_ORIGINS=http://localhost:5173,http://localhost:5174

🏃 Running the Application

Development Mode

Terminal 1 - Frontend (Vite dev server):

npm run dev

Runs on http://localhost:5173 with hot module replacement (HMR)

Terminal 2 - Backend (Express server):

npm run server

Runs on http://localhost:5000

Production Build

npm run build

Generates optimized production bundle in dist/

Preview Production Build

npm run preview

📁 Project Structure

MetaCore/
├── src/                          # React frontend
│   ├── App.jsx                   # Main application component
│   ├── main.jsx                  # React entry point
│   ├── views/                    # Feature-specific views
│   │   ├── AuthView.jsx          # Login/signup
│   │   ├── AIAgentView.jsx       # AI analysis interface
│   │   ├── DataView.jsx          # Manual data editor
│   │   ├── ForestView.jsx        # Forest plot visualization
│   │   ├── BiasView.jsx          # Bias detection
│   │   ├── ComplianceView.jsx    # Compliance tracking
│   │   ├── AdvancedView.jsx      # Advanced statistics
│   │   └── SavedAnalysesView.jsx # Analysis history
│   ├── context/                  # React Context
│   │   └── AuthContext.jsx       # Authentication state
│   └── utils/                    # Helper utilities
│       └── statistics.js         # Statistical calculations
│
├── server/                       # Express backend
│   ├── index.js                  # Server entry point
│   ├── config/
│   │   └── db.js                 # MongoDB connection
│   ├── models/
│   │   ├── User.js              # User schema
│   │   ├── Analysis.js          # Analysis schema
│   │   └── FileUpload.js        # File schema
│   ├── routes/
│   │   ├── auth.js              # Auth endpoints
│   │   ├── analyses.js          # Analysis CRUD
│   │   └── files.js             # File management
│   └── middleware/
│       └── auth.js              # JWT verification
│
├── package.json
├── vite.config.js
└── .env                         # Environment variables

🔌 API Endpoints

Authentication

• POST /api/auth/signup - Register new user
• POST /api/auth/login - Login with email/password
• POST /api/auth/google - Google OAuth login
• GET /api/auth/me - Get current user (requires auth)

Analyses

• GET /api/analyses - List user's analyses
• GET /api/analyses/:id - Get specific analysis
• POST /api/analyses - Create new analysis
• PUT /api/analyses/:id - Update analysis
• DELETE /api/analyses/:id - Delete analysis

Files

• GET /api/files - List uploaded files
• POST /api/files - Upload file
• GET /api/files/:id - Get file details
• DELETE /api/files/:id - Delete file

Health

• GET /api/health - Server health check

🔐 Security Features

• Password Encryption: bcryptjs with salt rounds
• JWT Tokens: 30-day expiration
• CORS Protection: Configurable allowed origins
• User Isolation: All data tied to authenticated user
• Email Verification: For OAuth providers
• Input Validation: Schema-level validation with Mongoose

📊 How to Use

1. Sign Up/Login: Create account or use Google OAuth
2. AI Agent: Upload study data and let AI generate comprehensive meta-analysis
3. Data Editor: Manually enter or edit study information
4. Generate Report: Create forest plots, bias assessments, and compliance reports
5. Save Analysis: Store completed analyses for later retrieval and modification
6. Export Results: Download data and visualizations for publication

🧪 Code Quality

npm run lint

Runs ESLint to check code style and potential issues

📝 Data Format

MetaCore expects CSV data with study-level information including:

• Study name/identifier
• Sample sizes (treatment/control)
• Event counts or continuous measures
• Mean and standard deviation (for continuous outcomes)

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is private. For licensing inquiries, please contact the repository owner.

💡 Support & Contact

For issues, questions, or suggestions, please:

• Open an issue on GitHub
• Contact: [repository owner]

🎯 Roadmap

• Export to publication formats (PDF, DOCX)
• Real-time collaborative editing
• Advanced statistical methods (network meta-analysis)
• Automated bias assessment scoring
• Integration with research databases
• Mobile app version

📚 Resources

• React Documentation
• Vite Guide
• Mongoose Docs
• Express.js Guide
• Meta-Analysis Methodology

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Created with ❤️ for the research community