Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm.

ABSTRACT: Hypertension is a critical risk factor and cause of mortality in cardiovascular diseases, and it remains a global public health issue. Therefore, understanding its mechanisms is essential for treating and preventing hypertension. Gene expression data is an important source for obtaining hypertension biomarkers. However, this data has a small sample size and high feature dimensionality, posing challenges to biomarker identification. We propose a novel deep graph clustering feature selection (DeepGCFS) algorithm to identify hypertension gene biomarkers with more biological significance. This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for trai

SUBMITTER: Li Z 

PROVIDER: S-EPMC11761172 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature