variant-confidence v0.1.0 adds calibration layer to genetic variant pathogenicity scores

Developer Pedro Sordo Martínez has released variant-confidence v0.1.0, an open-source Python tool that adds a calibrated confidence layer on top of existing protein variant-effect predictors such as AlphaMissense, ESM-1v, and EVE. The tool addresses a known gap between cross-validation accuracy and real-world reliability, where raw pathogenicity scores can be misleading without proper calibration. It applies techniques including Platt scaling, isotonic regression, and conformal prediction to produce uncertainty-aware outputs alongside Expected Calibration Error metrics. The project uses a temporal data split based on ClinVar release dates to prevent data leakage, and all 28 automated tests pass in the current release. Licensed under AGPL-3.0, the package is installable via pip, though users are advised to treat AlphaMissense data as non-commercial due to an unresolved licensing contradiction in its official documentation.