Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

ABSTRACT: Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance amo

SUBMITTER: Jain S 

PROVIDER: S-EPMC12122056 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature