Ancestry-aware variant interpretation
Deterministic ACMG/AMP engines that ask whether evidence is strong enough for a given ancestry — exposing gaps Western-centric databases hide.
We turn genomic data into evidence-informed therapeutic hypotheses — ancestry-aware variant interpretation and single-cell analysis of rare cancers, released as open-source tools.
Rather than treating models as black boxes, we build mechanistic AI and validate it against biological and clinical evidence — with a focus on rare and neglected entities. Where AI touches a clinical call, a deterministic engine decides and the model's reasoning stays auditable.
Deterministic ACMG/AMP engines that ask whether evidence is strong enough for a given ancestry — exposing gaps Western-centric databases hide.
Single-cell analysis of chordoma is surfacing phenotypes invisible to conventional metrics — from tumour heterogeneity to ferroptosis vulnerabilities.
A hybrid Mixture-of-Experts pairing random forests with graph neural networks, designed to fail safely under data scarcity.
VUS Lens audits the evidence; VUS Pipeline classifies for the tumour board. msep is method-agnostic and works on any cellular system, and HybridTox fails safely on scaffolds it has never seen.
Multi-scale entropy profiling — pathway-decomposed, any cellular system.
pip install msep
GitHub →
Ancestry-aware ACMG interpretation. 0 false-benign across 1,277 variants.
Deterministic ACMG/AMP classification for molecular tumour boards.