Identification and Evaluation of Key Biomarkers of Acute Myocardial Infarction by Machine Learning

Zhan, Zhenrun; Zhao, Tingting; Bi, Xiaodan; Jin, Yang; Han, Pengyong

doi:10.1007/978-3-031-13829-4_9

Cited by 2 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We improve the accuracy of disease-associated biomarker identification by combining machine learning algorithms and weighted gene co-expression network analysis (WGCNA). 10,11 WGCNA is an analytical approach that unites gene expression data and clinical information. The approach relies upon genomic, proteomic, and transcriptomic principles and methods to discover closely related gene/protein modules and analyze the correlation between modules and specific traits or disease phenotypes, thus providing an effective way to explore essential genes and significant mechanism correlations during disease development.…”

Section: Introductionmentioning

confidence: 99%

“…Bioinformatics is an up‐and‐coming discipline used to select various biomarkers in different kinds of oncological and non‐oncological diseases, 8,9 and this study downloaded a data set of AMI patients for the remaining data. We improve the accuracy of disease‐associated biomarker identification by combining machine learning algorithms and weighted gene co‐expression network analysis (WGCNA) 10,11 . WGCNA is an analytical approach that unites gene expression data and clinical information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying machine learning to screen for acute myocardial infarction‐related biomarkers and immune infiltration features and validate it clinically and experimentally

Zhan,

Han,

Tang

et al. 2023

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

Acute myocardial infarction (AMI) has been responsible for 8.5 million deaths worldwide each year over the past decade and is the leading cause of death. It is a severe illness worldwide and can happen in multiple age categories. Despite the significant progress in fundamental and clinical studies of AMI, biomarkers for AMI development have not been adequately investigated. The present research aimed to characterize potential new biomarkers of AMI by comprehensive analysis and to explore the immune infiltration characteristics of this pathophysiological process. In this study, we identified 68 DEGs and performed gene set enrichment analysis, GO, disease oncology, and KEGG analysis, and the results suggested that several functional signaling pathways and essential genes were strongly related to the onset and progression of AMI. In addition, combining multiple algorithms, FCER1G, CLEC4D, SRGN, and SLC11A1 were determined to be prospective biomarkers of AMI and showed good diagnostic value. Immuno‐infiltration analysis suggested that neutrophils, CD8+ T cells, monocytes, and M0 macrophages might be involved in the onset and progress of AMI. In conclusion, a combined approach was employed to select biomarkers associated with AMI and to probe the critical function of immune cells in the progression of AMI. In addition, clinical studies were applied to analyze the correlation between the occurrence of AMI and lipid dysregulation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%