0.997 AUROC on pathogenicity prediction for 839k ClinVar variants

EVEE achieves state-of-the-art performance on variant pathogenicity classification, outperforming existing methods.

Source paper

extracted_from

Explaining 4.2 million genetic variants with state-of-the-art, interpretable predictions

(2026) · Pearce, Michael · Dooms, Thomas · Yamamoto, Ryo · Meehl, Joshua +18

Neighborhood — ranked by edge-count

Papers (1)

paper

Explaining 4.2 million genetic variants with state-of-the-art, interpretable predictions
mentions

Claims (2)

claim

Evee outperforms existing methods for variant pathogenicity prediction
supports
Interpretive claim supported by the high AUROC findings.
EVEE provides mechanistic explanations for variant effects derived from model internals, not just pathogenicity calls.
supports
Core interpretability claim distinguishing EVEE from black-box prediction tools; applies interpretability for science.

Communities (2)

community

Mechanistic interpretability & model evaluation
members_of
Spans attention head decomposition, benchmark awareness, and genomic pathogenicity prediction via neural models.
Mechanistic explanation of genetic variant pathogenicity
members_of
Using genomic foundation model internals to generate disruption profiles that explain variant effects mechanistically, achieving 0.997 AUROC on ClinVar pathogenicity prediction.

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