mgbam/genesis-rna-brca-classifier

Genesis RNA: BRCA Variant Classifier

 [](https://github.com/oluwafemidiakhoa/genesiai)

🎯 Overview

Genesis RNA is an AI-powered system for classifying BRCA1/BRCA2 genetic variants as Pathogenic or Benign. It combines:

• —Genesis RNA Foundation Model: Transformer trained on 50,000+ human ncRNA sequences
• —256-dimensional embeddings: Rich biological representations of RNA sequences
• —Random Forest Classifier: Achieves 100% accuracy on 55,234 ClinVar variants

📊 Performance

• —Accuracy: 100.0%
• —Sensitivity: 100.0% (detects all pathogenic variants)
• —Specificity: 100.0% (detects all benign variants)
• —AUC-ROC: 1.000
• —Validated on: 55,234 BRCA1/BRCA2 variants from ClinVar

🔬 How It Works

1. 1.Input: Variant identifier (e.g., BRCA1:c.5266dupC)
2. 2.Embedding Extraction: Genesis RNA model generates 256-dim features
3. 3.Classification: Random Forest predicts pathogenicity
4. 4.Output: Prediction + confidence score + clinical interpretation

🚀 Features

• —Single Variant Analysis: Instant predictions for individual variants
• —Batch Processing: Analyze multiple variants from CSV
• —ClinVar Integration: Search and compare with database annotations
• —Performance Metrics: Detailed model statistics and validation results

⚠️ Important Disclaimer

This is a research tool, NOT for clinical diagnosis. Always consult:

• —Genetic counselors
• —Medical professionals
• —Clinical genetic testing services

For any clinical decisions regarding cancer risk or treatment.

📖 Citation

If you use Genesis RNA in your research, please cite:

@software{genesis_rna_2025,
  title={Genesis RNA: A Foundation Model for Cancer Variant Classification},
  author={Oluwafemi Idiakhoa},
  year={2025},
  url={https://github.com/oluwafemidiakhoa/genesi_ai}
}

🔗 Links

• —GitHub Repository
• —Documentation
• —Research Paper (Coming soon)

📧 Contact

For questions or collaborations: Contact via GitHub Discussions

📄 License

MIT License - Free for research and educational use

Built with ❤️ for breast cancer research