Nicholas K. Wang

Authored papers (2)

• EVEE: Interpretable variant effect prediction from genomic foundation model embeddings2026

  candidate
• Explaining 4.2 million genetic variants with state-of-the-art, interpretable predictions2026

  Evee, a variant-pathogenicity platform built on the Evo 2 genomic foundation model, achieves 0.997 AUROC on a 839,000-variant ClinVar benchmark — outperforming all previously reported methods — while simultaneously generating mechanistic "disruption profiles" that explain *why* a variant is predicted pathogenic rather than producing a scalar score alone. Zero-shot performance on insertions and deletions reaches 0.991 AUROC, a regime where many supervised methods degrade sharply. The system scales to approximately 4.2 million variants in total, including roughly 2 million variants of uncertain significance (VUS) for which no ground-truth labels exist and for which the disruption profiles constitute the primary clinical output. In a structured human evaluation, disruption profiles scored 3.8/5 for explanation quality against 2.8/5 for metadata-only baselines — a 36% relative gain in perceived explanatory value. The method extracts these profiles from Evo 2's internal representations via a sparse-feature-attribution pipeline developed jointly by Goodfire and Mayo Clinic. The paper argues this demonstrates that mechanistic interpretability applied to large biological sequence models can close the gap between black-box accuracy and clinician-usable reasoning, making genome-wide functional annotation tractable for direct clinical deployment on VUS that currently stall diagnostic workflows.

More papers — OpenAlex / S2

Recent mentions (2)

• papers-typed

  pearce-goodfire-evee-genetic-variants-2026.md
• papers-typed

  dooms-goodfire-covariance-pooling-2026.md