QGene – Hybrid Classical-Quantum Machine Learning for BRCA Variant Classification

📌 Project Overview

QGene is a hybrid machine learning system designed to classify BRCA1 and BRCA2 gene mutations as:

• Benign (Harmless)
• Pathogenic (Disease-causing)

The system combines:

• Classical Machine Learning (Random Forest, SVM)
• Quantum Machine Learning (VQC, QSVM using Qiskit)
• Hybrid Ensemble Modeling

The final goal is to build a research-grade prototype with a web interface for mutation prediction.

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📊 Week 1 Progress (Completed)

✅ Environment setup (Python 3.12 + Anaconda + VS Code)
✅ Installed required ML & Quantum libraries
✅ Downloaded ClinVar dataset
✅ Filtered BRCA1 & BRCA2 variants
✅ Cleaned dataset (37,362 variants)
✅ Created binary classification labels
✅ Performed exploratory data analysis
✅ Generated data visualizations

Dataset is balanced (~49% benign, ~51% pathogenic) with no missing values.

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📂 Project Structure