Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure.

ABSTRACT:

Objective

This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease.

Methods

The heart failure microarray datasets and RNA-seq datasets have been downloaded from the public database. Differentially expressed genes (DE genes) are screened out, and then, we analyze their biological functions and pathways. Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. Then, external independent RNA-seq datasets evaluate the diagnostic value of gene signals. Finally, the convolu

SUBMITTER: Jiang C 

PROVIDER: S-EPMC9206587 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature