A robust ensemble feature selection approach to prioritize genes associated with survival outcome in high-dimensional gene expression data.

ABSTRACT: Exploring features associated with the clinical outcome of interest is a rapidly advancing area of research. However, with contemporary sequencing technologies capable of identifying over thousands of genes per sample, there is a challenge in constructing efficient prediction models that balance accuracy and resource utilization. To address this challenge, researchers have developed feature selection methods to enhance performance, reduce overfitting, and ensure resource efficiency. However, applying feature selection models to survival analysis, particularly in clinical datasets characterized by substantial censoring and limited sample sizes, introduces unique challenges. We propose a robust ensemble feature selection approach integrated with group Lasso to identify compelling features an

SUBMITTER: Le P 

PROVIDER: S-EPMC11786965 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature