Sharif Digital Repository

Genome mutations whose effects are not specified pose one of the challenges in identifying genetic diseases. Utilizing wet lab tests to detect the pathogenicity of variants can be time-consuming and fiscally expensive. A rapid and cost-effective solution to this problem is the use of machine learning-based variant effect predictors, which have the ability to determine whether a mutation is pathogenic or not. The objective of this research is to predict the pathogenicity of genome variations. The proposed model exclusively utilizes the protein sequence as its input feature and does not have access to other protein features. The data used to construct the model comprises mutations with...