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Quantum Neural Networks (QNNs) for Genomic Pattern Detection in Personalized Medicine

Project Status
License
Intel Tools

📑 Overview

This project introduces Quantum Neural Networks (QNNs) to analyze genomic data for personalized medicine. With the rise of genetic sequencing, QNNs can detect complex patterns in genetic variants to predict disease risks, drug responses, and optimal treatment paths. By leveraging quantum computation, the project tackles the high-dimensional complexity of genomic pattern recognition, which classical neural networks struggle to handle efficiently.

🚀 Project Goals

  • Predict Disease Risks: Identify predispositions to diseases like cancer or cardiovascular conditions based on genetic mutations.
  • Drug Recommendations: Suggest optimal medications and treatments tailored to individual genetics.
  • Personalized Health Advice: Provide lifestyle recommendations based on patient profiles and genomic patterns.

🛠 Tools & Technologies

  • Intel Quantum Simulator: Develop and test quantum neural networks.
  • OpenVINO Toolkit: Deploy classical neural networks for efficient edge inference.
  • Intel DevCloud: Train models on high-performance hardware.

📊 System Architecture

1. Data Preprocessing

  • Genomic Feature Extraction: Extract SNPs, mutations, and biomarkers.
  • Dimensionality Reduction: Use classical neural networks to reduce data size.
  • Quantum Encoding: Apply quantum encoding techniques (e.g., amplitude encoding) for QNN input.

2. Hybrid QNN-ML Model

  • Classical Neural Network (CNN): Extract features and perform dimensionality reduction.
  • Quantum Neural Network (QNN): Detect complex patterns and correlations in genomic data.
  • Hybrid Integration: Combine CNN output with QNN for final predictions.

3. Prediction & Recommendations

  • Disease Risk Prediction: Identify high-risk genetic predispositions.
  • Drug Recommendations: Suggest medications based on individual genetic variants.
  • Lifestyle Recommendations: Provide personalized advice for healthier living.

⚙️ Workflow

  1. Genomic Data Preprocessing: Extract SNPs and biomarkers from datasets like 1000 Genomes Project and TCGA.
  2. Hybrid Model Execution: Use CNN for feature extraction and QNN for pattern detection.
  3. Real-time Inference: Deploy the model on edge devices using OpenVINO.
  4. Prediction & Output: Display disease risks, drug suggestions, and lifestyle recommendations.

TESTING:

QNNGPD