| Abstract |
Prion protein misfolding causes Creutzfeldt-Jakob Disease (CJD), a fatal neurodegenerative disorder.
However, experimentally testing the pathogenicity of PRNP variants is time-consuming, costly, and complex. This project uses a nonlinear synaptic pruning and dendritic integration transformer-based machine learning model (NSPDI-SNN) to predict which prion protein sequences misfold. The approach demonstrates how computational modeling can provide a fast, interpretable tool for studying disease-associated protein variants.
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