Abstract:Background: The role of immune cells in the pathogenesis of ischaemic heart failure (IHF) is well-established. However, identifying key diagnostic candidate genes in patients with IHF remains a challenge. Therefore, this study aimed to use bioinformatics and machine learning algorithms to identify potential diagnostic genes for IHF. Methods: Two IHF datasets were obtained from the GEO database, and key genes for IHF were identified using Limma and WGCNA. Functional enrichment analysis was performed to explore … Show more
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