Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.

ABSTRACT:

Background

The clinical potential of single nucleotide polymorphisms (SNPs) in prostate cancer (PCa) diagnosis has been extensively explored using conventional statistical and machine learning approaches. However, the predictive power and interpretability of these methods remain inadequate for clinical translation, primarily due to limited generalization across high-dimensional SNP datasets. This study addresses the contested diagnostic utility of SNPs by integrating interpretable feature selection with deep learning to enhance both classification performance and biological relevance.

Methods

We propose an interpretable deep feature selection framework designed to enhance both the classification performance and biological relevance of SNP markers in distinguishing between be

SUBMITTER: Chen Z 

PROVIDER: S-EPMC12481780 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature