# Mial

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Haonan Feng](https://cerebralvalley.ai/u/HNF)
- **GitHub:** https://github.com/hfeng682/SGE_clinical/
- **Demo video:** https://www.youtube.com/watch?v=Mimw797N_mA
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/247

We developed a computational method that converts two-channel saturation genome editing (SGE) screens, which measure both cell survival and mRNA abundance, into mechanism-labelled, error-bounded ACMG/AMP clinical evidence codes. It is a three-stage pipeline that assigns each variant a mechanistic call (protein loss vs RNA-mediated loss), derives its clinical strength from error-bounded OddsPath ratios (no distributional assumptions, explicit upper bounds), and adjusts transferability across tissues based on mechanism. RNA-mediated losses receive one ACMG tier downgrade unless corroborated by independent evidence (cross-condition reproducibility or AlphaGenome splice prediction gated by disease-tissue expression).
When applied across 35,333 variants in five genes (BARD1, BRCA1, PALB2, RAD51D, VHL), the method grades 4,569 previously-uncertain variants: notably 2,820 protein-blind splice-region variants, the clinically hardest class. It downgradus 611 RNA-route losses where tissue transfer lacked corroboration. External validation shows 79–100% agreement in independent screens. The RNA channel identifies confirmed splice-altering variants that SpliceAI misses at standard thresholds; Wilks-certified thresholds improve precision from 0.84 to 0.89 without loss of recovery.
This work is significant because functional assays provide strong clinical evidence but are underused for non-coding variants. This method reveals mechanism which dictates tissue transferability, and replaces point estimates with finite-sample error guarantees, reducing false clinical classifications.

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