Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.

ABSTRACT: Accurate classification of genetic variants is critical for precision medicine, particularly hereditary diseases such as breast cancer. However, widely used tools like MutPred and Combined Annotation Dependent Depletion (CADD) offer genome-wide pathogenicity predictions that often overlook disease-specific variant behavior, limiting their clinical utility. This study addresses that gap by training and benchmarking nine machine learning (ML) models-including ensemble and baseline classifiers-on a breast cancer gene-specific dataset rich in conservation scores, functional annotations, and allele frequency features. Among all models, the Extra Trees model achieved the highest performance, with an accuracy of 0.999 and a 95% confidence interval of (0.998-1.000). recursive feature elimination i

SUBMITTER: Ahmad RM 

PROVIDER: S-EPMC12632190 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature