README.md

metadata

title: Mutation Explainability Intelligence System
emoji: 🧬
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
license: mit
tags:
  - genomics
  - bioinformatics
  - explainability
  - pathogenicity
  - splice
  - XAI

Mutation Explainability Intelligence System

Explainability-first three-model ensemble for SNV pathogenicity prediction.

The system answers five core questions before presenting any prediction score:

1. Why was this variant predicted pathogenic / benign?
2. Which internal model signals drove that decision?
3. Is the signal localised at the mutation site?
4. Did removing the mutation change the prediction?
5. Do multiple models agree mechanistically?

Models

Model	Repo	Architecture
Splice	nileshhanotia/mutation-predictor-splice	MutationPredictorCNN_v2
V4	nileshhanotia/mutation-predictor-v4	MutationPredictorCNN_v4
Classic	nileshhanotia/mutation-pathogenicity-predictor	MutationPredictorClassic

Input

• Chromosome, Position (hg38), Ref base, Alt base, Exon/Intron flag
• Sequence fetched automatically from Ensembl REST API (99-bp window)

Explainability Signals

• conv3 activation norm profiles — per-nucleotide activation intensity
• Mutation-centred activation peak — peak at mutation site vs mean
• Gradient attribution maps — input-gradient backward pass
• Splice aura distance — proximity to GT/AG splice dinucleotides
• Counterfactual delta — all alternative bases tested
• Feature ablation — splice / region / mutation / sequence groups
• Cross-model locality score — Pearson correlation of activation profiles
• Explainability Strength Score — 0–1 composite quality metric

Confidence Levels

High / Moderate / Low based on model agreement, ESS, and counterfactual magnitude.

Disclaimer

For research use only. Not a clinical diagnostic tool.