variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores

variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores

State-of-the-art variant-effect models are accurate in cross-validation but their scores are poorly calibrated on temporal data. variant-confidence adds an auditable calibration layer on top of existing predictors — it does not train a new model.

The problem: accuracy is not trust

Protein variant-effect predictors (AlphaMissense, ESM-1v, EVE) report pathogenicity scores, but a clinician or researcher needs to know how much to trust the number, not just its rank. The gap is calibration, not accuracy:

AnnotateMissense (2026) reports MCC 0.94 in cross-validation, dropping to 0.76 on temporal ClinVar, accuracy 0.8798.