PON-P3: Accurate Prediction of Pathogenicity of Amino Acid Substitutions.

ABSTRACT: Different types of information are combined during variation interpretation. Computational predictors, most often pathogenicity predictors, provide one type of information for this purpose. These tools are based on various kinds of algorithms. Although the American College of Genetics and the Association for Molecular Pathology guidelines classify variants into five categories, practically all pathogenicity predictors provide binary pathogenic/benign predictions. We developed a novel artificial intelligence-based tool, PON-P3, on the basis of a carefully selected training dataset, meticulous feature selection, and optimization. We started with 1526 features describing variations, their sequence and structural context, and parameters for the affected genes and proteins. The final random boo

SUBMITTER: Kabir M 

PROVIDER: S-EPMC11899954 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature