PhenoPredict - Genetic and Image-Based Prediction Platform

Overview

PhenoPredict is an innovative web-based platform that allows users to upload genetic data or an image of a person's face to receive predictions about their appearance, behavior, ancestry, and potential health risks. The application is built using a microservices architecture to ensure scalability, flexibility, and ease of maintenance.

Table of Contents

1. Project Structure
2. Microservices Overview
   • User Interface Service
   • Authentication and Authorization Service
   • Genetic Data Processing Service
   • Image Processing Service
   • Prediction and Analysis Service
   • Reporting and Visualization Service
   • Data Storage Service
   • Notification Service
   • API Gateway
3. Technology Stack
4. Getting Started
   • Prerequisites
   • Installation
   • Running the Services
5. Contributing
6. License

Project Structure

The project is divided into several microservices, each handling a specific aspect of the application. These services communicate through an API Gateway and use a variety of technologies to perform their tasks.

project-root/
├── ui-service/                      # Frontend React application
├── auth-service/                    # User authentication and authorization service
├── genetic-data-processing-service/ # Genetic data parsing and processing
├── image-processing-service/        # Image analysis and facial recognition
├── prediction-service/              # Predictive modeling for appearance, behavior, and health
├── reporting-service/               # Report generation and data visualization
├── data-storage-service/            # Database and storage management
├── notification-service/            # Email and notification handling
└── api-gateway/                     # API Gateway for routing and communication

Microservices Overview

User Interface Service

• Purpose: Provides the user interface for uploading genetic data and images, viewing predictions, and managing accounts.
• Technology: React.js, Redux, Axios, Material-UI.

Authentication and Authorization Service

• Purpose: Manages user authentication, registration, and authorization using JWT tokens.
• Technology: Python, Django/Flask, OAuth2, JWT.

Genetic Data Processing Service

• Purpose: Processes and analyzes uploaded genetic data, extracting relevant SNPs for predictions.
• Technology: Python, Biopython.

Image Processing Service

• Purpose: Analyzes uploaded images to extract facial features and perform ancestry, behavior, and health predictions.
• Technology: Python, OpenCV, TensorFlow/PyTorch.

Prediction and Analysis Service

• Purpose: Runs machine learning models to predict appearance, behavior, ancestry, and health risks based on genetic or image data.
• Technology: Python, Scikit-learn, TensorFlow/PyTorch.

Reporting and Visualization Service

• Purpose: Generates detailed reports and visualizations based on the analysis results.
• Technology: Node.js/Python, D3.js, Plotly.

Data Storage Service

• Purpose: Manages the storage of user data, including genetic data, images, and analysis results.
• Technology: PostgreSQL, AWS S3, MongoDB/DynamoDB.

Notification Service

• Purpose: Handles email and in-app notifications to inform users about the status of their data processing and results.
• Technology: Node.js/Python, AWS SES, SendGrid.

API Gateway

• Purpose: Routes requests from the frontend to the appropriate backend services and handles authentication and authorization.
• Technology: AWS API Gateway, Kong, Express.js.

Technology Stack

• Frontend: React.js, Redux, Material-UI
• Backend: Python (Django/Flask), Node.js
• Machine Learning: Scikit-learn, TensorFlow, PyTorch
• Database: PostgreSQL, MongoDB, AWS S3
• API Gateway: AWS API Gateway, Kong
• Authentication: OAuth2, JWT
• Containerization: Docker, Kubernetes
• CI/CD: Jenkins, GitHub Actions, AWS CodePipeline
• Hosting: AWS (EC2, S3, Lambda)

Getting Started

Prerequisites

• Node.js: v12.x or later
• Python: v3.7 or later
• Docker: v19.x or later
• Git: v2.x or later
• AWS Account (optional for cloud deployment)

Installation

Clone the repository and navigate to the desired service directory:

git clone https://github.com/iebgit/phenopredict.git
cd phenopredict

Install dependencies for each service:

cd ui-service
npm install

cd ../auth-service
pip install -r requirements.txt

# Repeat for other services

Running the Services

1. Start Docker: Ensure Docker is running on your machine.

2. Start Services:

   • For each service, use Docker Compose to build and run the containers:

     docker-compose up --build

3. Access the Application:

   • Navigate to http://localhost:3000 to access the User Interface Service.
   • The API Gateway will route requests to the appropriate backend services.

Contributing

Contributions are welcome! Please follow these steps to contribute:

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

License

This project is licensed under the MIT License. See the LICENSE file for details.