Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data.

ABSTRACT: Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing structured data, particularly in domains where relationships and interactions between entities are key. By leveraging the inherent graph structure in datasets, GNNs excel in capturing complex dependencies and patterns that traditional neural networks might miss. This advantage is especially pronounced in the field of computational biology, where the intricate connections between biological entities play a crucial role. In this context, Our work explores the application of GNNs to single-cell RNA sequencing (scRNA-seq) data, a domain characterized by complex and heterogeneous relationships. By extracting ligand-receptor (L-R) associations from LIANA and constructing Cell-Cell association networks with varying edge hom

SUBMITTER: Duan L 

PROVIDER: S-EPMC12104675 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature