A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma.

ABSTRACT: Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random For

SUBMITTER: Abdelwahab O 

PROVIDER: S-EPMC9447897 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature