MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.

Abstract excerpt

The interpretation and classification of nonsynonymous single nucleotide variants (nsSNVs) remains a significant challenge in clinical genomics particularly for variants of uncertain significance (VUS). Although many computational methods have been developed for predicting variant pathogenicity, their performance remains limited and often lacks interpretability. To address these limitations, we developed MetaXVP...

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