Abstract
Genetic screening of rare diseases allows identification of the responsible gene(s) in about 50% of patients. The remaining cases are in a diagnostic deadlock as current knowledge fails to identify the correct gene or determine if the detected variant on the gene is pathogenic. These are named "variants of unknown significance" (VUS). In the case of neuromuscular diseases, the RYR1 gene is often implicated, with the majority of variants classified as VUS, requiring reliable classification to help patient diagnosis. Our project aims to create an efficient classification pipeline, integrating artificial intelligence, structural biology data, and functional analyses to enhance genetic diagnosis of RYR1-related diseases.
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