Uncovering hub genes and immunological characteristics for heart failure utilizing RRA, WGCNA and Machine learning.

ABSTRACT:

Background

Heart failure (HF) is a major public health issue with high mortality and morbidity. This study aimed to find potential diagnostic markers for HF by the combination of bioinformatics analysis and machine learning, as well as analyze the role of immune infiltration in the pathological process of HF.

Methods

The gene expression profiles of 124 HF patients and 135 nonfailing donors (NFDs) were obtained from six datasets in the NCBI Gene Expression Omnibus (GEO) public database. We applied robust rank aggregation (RRA) and weighted gene co-expression network analysis (WGCNA) method to identify critical genes in HF. To discover novel diagnostic markers in HF, three machine learning methods were employed, including best subset regression, regularization technique, and s

SUBMITTER: Tu D 

PROVIDER: S-EPMC10869931 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature