Gene Screening in High-Throughput Right-Censored Lung Cancer Data.

ABSTRACT:

Background

Advances in sequencing technologies have allowed collection of massive genome-wide information that substantially advances lung cancer diagnosis and prognosis. Identifying influential markers for clinical endpoints of interest has been an indispensable and critical component of the statistical analysis pipeline. However, classical variable selection methods are not feasible or reliable for high-throughput genetic data. Our objective is to propose a model-free gene screening procedure for high-throughput right-censored data, and to develop a predictive gene signature for lung squamous cell carcinoma (LUSC) with the proposed procedure.

Methods

A gene screening procedure was developed based on a recently proposed independence measure. The Cancer Genome Atlas (TCGA) d

SUBMITTER: Ke C 

PROVIDER: S-EPMC10100230 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature