Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

ABSTRACT: Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction

SUBMITTER: Jaksik R 

PROVIDER: S-EPMC11011391 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature