Integration of gene expression and DNA methylation data using MLA-GNN for liver cancer biomarker mining.

ABSTRACT: The early symptoms of hepatocellular carcinoma patients are often subtle and easily overlooked. By the time patients exhibit noticeable symptoms, the disease has typically progressed to middle or late stages, missing optimal treatment opportunities. Therefore, discovering biomarkers is essential for elucidating their functions for the early diagnosis and prevention. In practical research, challenges such as high-dimensional features, low sample size, and the complexity of gene interactions impact the reliability of biomarker discovery and disease diagnosis when using single-omics approaches. To address these challenges, we thus propose, Multi-level attention graph neural network (MLA-GNN) model for analyzing integrated multi-omics data related to liver cancer. The proposed protocol are usi

SUBMITTER: Lu CY 

PROVIDER: S-EPMC11701154 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature