Calibration and evaluation of machine-learning algorithms for missense variant classification under ACMG/ClinGen recommendations

Missense variants represent a large proportion of variants of uncertain significance (VUS) in clinical genetics. The ClinGen framework now enables quantitative use of in silico predictors for variant classification (PP3/BP4), but newly developed machine-learning-based tools such as AlphaMissense (AM) and PrimateAI-3D (PAI3D) have not been evaluated in this context using real-world clinical data.