EngGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection.

ABSTRACT: Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph Neural Networks (GNNs) offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated one, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages

SUBMITTER: Yang T 

PROVIDER: S-EPMC12869420 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature