---
title: Mutation Explainability Intelligence System
emoji: 🧬
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.44.1
python_version: "3.11"
app_file: app.py
pinned: false
license: mit
tags:
  - bioinformatics
  - genomics
  - explainability
  - clinical-genomics
  - pytorch
  - xai
short_description: Explanation-first tri-model genomic variant analysis
---

# 🧬 Mutation Explainability Intelligence System

**Explanation before prediction. Always.**

## Models used

| Model | Repo |
|---|---|
| Splice disruption (MutationPredictorCNN_v2) | `nileshhanotia/mutation-predictor-splice` |
| Protein context v4 (MutationPredictorCNN_v2) | `nileshhanotia/mutation-predictor-v4` |
| Classic pathogenicity (MutationPredictorCNN) | `nileshhanotia/mutation-pathogenicity-predictor` |

## Internal signals extracted

- **conv3 activation norm profile** — per-nucleotide CNN signal (99 positions)
- **Gradient attribution map** — input-gradient backward pass
- **Mutation-centered peak** — activation value at the specific mutation position
- **Splice aura distance** — distance to nearest GT donor / AG acceptor
- **Counterfactual delta** — probability range across all alternative substitutions
- **Feature ablation response** — causal effect of zeroing splice / region / mutation features
- **Risk tier classification** — PATHOGENIC → BENIGN

## ⚠️ Disclaimer

For research use only. Not a clinical diagnostic tool.