---
language: en
license: apache-2.0
library_name: pytorch
pipeline_tag: text-classification
tags:
- genomics
- mutation
- pathogenicity
- splice
- explainable-ai
- biology
- clinical-ai
---

# 🧬 MutationPredictorCNN_v2 — Splice-Aware Pathogenicity Predictor

## Model Summary

MutationPredictorCNN_v2 is a splice-aware convolutional neural network designed to predict pathogenicity of single nucleotide variants using genomic sequence context and splice-aware features.

Supports built-in explainability:

• CNN activation heatmap  
• Gradient attribution  
• Counterfactual mutation analysis  
• Feature ablation analysis  
• Splice distance analysis  

Validation accuracy: 74.8%

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## Intended Use

Research use cases:

• Genomic variant interpretation  
• Explainable AI research  
• Variant prioritization  
• Educational and academic research  

NOT intended for clinical diagnostic use.

---

## Model Architecture

CNN-based architecture:

Input: 1106 features  
Output: Pathogenicity probability  

Explainability heads:

• Mutation importance  
• Region importance  
• Splice importance  

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## Training Data

Source: ClinVar

Dataset size:

100,000 variants  
50,000 pathogenic  
50,000 benign  

Sequence window: 99 bp

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## Performance

Validation accuracy:

74.8%

Balanced dataset.

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## Explainability

Provides multi-level explainability:

• Activation heatmap  
• Mutation rank percentile  
• Gradient attribution map  
• Counterfactual analysis  
• Feature ablation analysis  

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## Limitations

Supports only:

• Single nucleotide variants  
• 99 bp context window  

Does not include:

• Conservation scores  
• Protein structure  
• Expression context  

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## Disclaimer

⚠ Research use only  
Not a clinical diagnostic tool  

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## Maintainer

Nilesh Hanotia