Identification of hub genes in heart failure by integrated bioinformatics analysis and machine learning.

ABSTRACT:

Objective

To screen feature genes of heart failure patients through machine learning methods, in order to identify characteristic genes driving heart failure and investigate the progression of heart failure.

Methods

Heart failure patient samples were downloaded from the public database GEO (Gene Expression Omnibus), including the datasets GSE116250, GSE120895, and GSE59867. GSE116250 and GSE120895 were used as the testing set, while GSE59867 was used as the validation set. LASSO regression analysis and SVM-RFE were utilized to identify feature genes.

Results

Analysis showed that among the differentially expressed genes between normal and heart failure patients, 9 genes were upregulated and 10 genes were downregulated. ROC curve analysis in the training set showed that

SUBMITTER: Wang T 

PROVIDER: S-EPMC10796662 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature