Machine-learning-based determination of sex-related bladder cancer biomarkers.

ABSTRACT: Bladder cancer exhibits sex-specific behavior, occurring more frequently in males but progressing to advanced stages more commonly in females. The activation of sex hormone receptors may explain these differences, but the exact genetic drivers remain poorly understood. Furthermore, current bladder cancer biomarkers have inconsistent sensitivities and specificities in practice, making early diagnosis a challenge. This study approaches bladder cancer biomarker discovery through machine learning techniques on gender and disease-stratified RNA-seq data. Training sets limited to differentially expressed genes were subjected to four different feature selection methods: differential gene expression analysis adjusted p-value, recursive feature elimination with support vector machine, logistic regr

SUBMITTER: Pizzi JR 

PROVIDER: S-EPMC12767351 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature