首页|Evaluation of enzyme activity predictions for variants of unknown significance i n Arylsulfatase A
Evaluation of enzyme activity predictions for variants of unknown significance i n Arylsulfatase A
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “Continued advances in variant effect prediction are necessary to demonstrate th e ability of machine learning methods to accurately determine the clinical impac t of variants of unknown significance (VUS). Towards this goal, the ARSA Critica l Assessment of Genome Interpretation (CAGI) challenge was designed to character ize progress by utilizing 219 experimentally assayed missense VUS in the Arylsul fatase A (ARSA) gene to assess the performance of community-submitted prediction s 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 am ong submissions. “Furthermore, the study observed that state-of-the-art deep learning methods pro vided small but statistically significant improvement in predictive performance compared to less elaborate techniques.
ArylsulfatasesCyborgsEmerging Techno logiesEnzymes and CoenzymesGeneticsHydrolasesMachine LearningSulfatase s
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